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Engineering Research Express

As a society-owned publisher with a legacy of serving scientific communities, we are committed to offering a home to all scientifically valid and rigorously reviewed research. In doing so, we aim to accelerate the dissemination of scientific knowledge and the advancement of scholarly communications to benefit all.

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Engineering Research Express (ERX) is a broad, multidisciplinary journal devoted to publishing new experimental and theoretical research covering topics extending across all areas of engineering science including interdisciplinary fields.

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Mohammed Asadullah Khan et al 2021 Eng. Res. Express 3 022005

Magnetic field sensors are an integral part of many industrial and biomedical applications, and their utilization continues to grow at a high rate. The development is driven both by new use cases and demand like internet of things as well as by new technologies and capabilities like flexible and stretchable devices. Magnetic field sensors exploit different physical principles for their operation, resulting in different specifications with respect to sensitivity, linearity, field range, power consumption, costs etc. In this review, we will focus on solid state magnetic field sensors that enable miniaturization and are suitable for integrated approaches to satisfy the needs of growing application areas like biosensors, ubiquitous sensor networks, wearables, smart things etc. Such applications require a high sensitivity, low power consumption, flexible substrates and miniaturization. Hence, the sensor types covered in this review are Hall Effect, Giant Magnetoresistance, Tunnel Magnetoresistance, Anisotropic Magnetoresistance and Giant Magnetoimpedance.

Erteza Tawsif Efaz et al 2021 Eng. Res. Express 3 032001

Thin-film solar cells are preferable for their cost-effective nature, least use of material, and an optimistic trend in the rise of efficiency. This paper presents a holistic review regarding 3 major types of thin-film solar cells including cadmium telluride (CdTe), copper indium gallium selenide (CIGS), and amorphous silicon ( α -Si) from their inception to the best laboratory-developed module. The remarkable evolution, cell configuration, limitations, cell performance, and global market share of each technology are discussed. The reliability, availability of cell materials, and comparison of different properties are equally explored for the corresponding technologies. The emerging solar cell technologies holding some key factors and solutions for future development are also mentioned. The summarized part of this comparative study is targeted to help the readers to decipher possible research scopes considering proper applications and productions of solar cells.

Eshaan Gupta et al 2022 Eng. Res. Express 4 025039

The objective of this research paper is to design, simulate and compare components of a race car braking system. The Racecar is an FSAE car that is designed around the rules and regulations of the FSAE rulebook, the main aim of this project is to make components lightweight and improve their performance as compared to their OEM counterparts. The braking system involves the mathematical calculation of pedal ratio, brake torque, heat generated in brake discs, and required clamping force using MATLAB to achieve peak deceleration after which these values would be used to design and simulate the components. The paper presents some innovative new ideas applied in an FSAE car and also involves designing techniques like topology optimization which was done using Altair inspire. Finally, all the components were designed in Solidworks, and simulations like Factor of safety, von mises stress, and strain were performed using ANSYS 18.1. The paper also compares the work of other authors as well and explains the differentiating factors between our and their design.

Wubshet Ayalew et al 2024 Eng. Res. Express 6 035212

Path planning is an important task for mobileF service robots. Most of the available path-planning algorithms are applicable only in static environments. Achieving path planning becomes a difficult task in an unknown, dynamic environment. To solve the path planning problem in an unknown dynamic environment, this paper proposes a Bidirectional Rapidly-exploring Random Tree Star-Dynamic Window Approach (BRRT*-DWA) algorithm with Adaptive Monte Carlo Localization (AMCL). Bidirectional Rapidly-exploring Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used as a localization technique. The robot can navigate using the map file of the unknown environment created by Simultaneous Localization And Mapping (SLAM) and the data from the Light Detection and Ranging (LiDAR) sensor while avoiding dynamic and static obstacles. In addition, the object identification algorithm You Only Look Once (YOLO) was adopted, trained, and used for the robot to recognize objects and people. Results obtained from both simulation and experiment show the proposed method can achieve better performance in a dynamic environment compared with other state-of-the-art algorithms.

Samsul Hafiz et al 2024 Eng. Res. Express 6 035313

Electrical energy storage (EES) plays a crucial role in various power applications. Voltage imbalance is a common issue that can negatively affect the efficiency, reliability, and safety of EESs. Several types of voltage balancing (VB) circuits have been proposed in much of the literature. Among these VB circuits, switched capacitor (SC)-based circuits have attracted significant interest due to their efficiency, cost-effectiveness, compact size, and ease of control, but their balancing performance is not yet satisfactory. As a result, structural modifications in SC-based circuits have been widely proposed to improve balancing performance. However, not all of these circuit structures have been implemented into a resonant switched capacitor (RSC)-based voltage balancing, which has higher efficiency. Hence, this study aims to assess the efficiency of RSC-based VB circuits by conducting analog simulation using the Matlab Simulink software. This research evaluates the performance of the VB circuit not only in terms of its speed and efficiency, but also in terms of its energy distribution. The results show that the delta structure is the fastest in terms of balancing speed when completing the balancing process, followed by the mesh structure and the parallel structure. The best energy distribution is produced by a parallel structure, as indicated by the change in voltage of all battery cells always moving towards a convergent value, regardless of the variations in initial imbalance conditions. Meanwhile, other circuit structures distribute energy randomly, allowing the voltage of the battery cells to change not directly towards a convergent value. Lastly, the paper summarizes the balancing speed, efficiency, circuit complexity, and quality of energy distribution.

Shekinah Archita S and Ravi V 2024 Eng. Res. Express 6 032203

The memristor is regarded as one of the promising possibilities for next-generation computing systems due to its small size, easy construction, and low power consumption. Memristor-based novel computing architectures have demonstrated considerable promise for replacing or enhancing traditional computing platforms that encounter difficulties in the big-data era. Additionally, the striking resemblance between the mechanisms governing the programming of memristance and the manipulation of synaptic weight at biological synapses may be used to create unique neuromorphic circuits that function according to biological principles. Nevertheless, getting memristor-based computing into practice presents many technological challenges. This paper reviews the potential for memristor research at the device, circuit, and system levels, mainly using memristors to demonstrate neuromorphic computation. Here, the common issues obstructing the development and widespread use of memristor-based computing systems are also carefully investigated. This study speculates on the prospective applications of memristors, which can potentially transform the field of electronics altogether.

Lingling Liu et al 2024 Eng. Res. Express 6 035006

In this work, Ni-W-Ti 2 AlC composite coatings with anti-wear and anti-corrosion abilities were prepared using direct current electrodeposition. The study primarily focuses on the influence of different current densities on the structure and properties of Ni-W-Ti 2 AlC composite coatings. The microstructural, mechanical properties, and electrochemical performances were studied by using SEM, XRD, EDS, and Tafel polarization techniques. The results indicated that Ti 2 AlC is successfully co-deposited into the Ni-W coating, and the average grain size of the Ni-W-Ti 2 AlC composite coating is 15.2 nm. The significant improvement in hardness, wear resistance, and corrosion resistance of Ni-W-Ti 2 AlC composite coating is attributed to the effects of solid solution strengthening and the uniform distribution of the microstructure. The suitable current density has a great influence on the properties. The microhardness of Ni-W-Ti 2 AlC composite coatings was increased by 99.59% at a current density of 5 A dm −2 compared to 3 A dm −2 . At the same time, the friction coefficient decreased by 13.89%.

Samrat Hazra et al 2024 Eng. Res. Express 6 035525

In this experimental work, magnesium nanocomposite reinforced with various percentage of boron carbide (0.5%, 1.0%, 1.5%, 2.0% by volume) is fabricated using conventional powder metallurgy technique. Characterisation and wear test of the fabricated composite have been performed using scanning electron microscopy, tribology test monitor respectively. It is observed that addition of B 4 C increases the hardness and wear resistance linearly. Addition of 2% B 4 C produces the hardness of the nanocomposites as high as 57.93 ± 4.9 Hv. Furthermore, the nanocomposites displayed improved wear resistance and lower friction coefficient compared to the base magnesium. The influence of sliding speed on these properties has been systematically presented. The paper provides an in-depth exploration of wear mechanisms through a meticulous analysis of SEM micrographs of worn surfaces. This work offers valuable insights into the microstructural characteristics and tribological behaviour of Mg-B 4 C nanocomposites. These findings underscore the potential application of Mg-B 4 C nanocomposites in the development of lightweight wear-resistant materials. The impact of incorporating minute amounts of nano-sized boron carbide into Mg, on its behaviour under small loads and repeated scratching has been investigated in this study. Moreover, introspection of the wear mechanisms and the effectiveness of the powder metallurgy technique in fabricating composite materials is presented.

Pichingla Kharei et al 2023 Eng. Res. Express 5 012001

High Electron Mobility Transistors (HEMT) made of aluminum gallium nitride/gallium nitride (AlGaN/GaN) have become a major focus for all electronic devices based on gallium nitride due to its excellent system characteristics. AlGaN/GaN HEMTs have severe problems that degrade their performance and the drain current collapse (CC) is one of them. During switching operations, the CC increases the on-resistance (R ON ) leading to an increase in device loss and temperature. This review features the basics related to the CC in HEMT and its significance in performance degradation. This paper is concerned with the various advancements reported in recent years to suppress CC in GaN HEMT. Various techniques such as passivation, illumination, free-standing GaN substrate, GaN cap layer including high resistivity GaN cap layer, device structure, surface treatment and deposition techniques, buffer design, and field plates (FP) have been introduced by various researchers to combat CC. This review analysis will help researchers to employ suitable techniques in their HEMT design for future development.

Nathan Totorica et al 2024 Eng. Res. Express 6 035326

This paper compares different types of Gate All Around (GAA) FET structures using TCAD simulation, including Lateral Nanosheet, Lateral Nanowire, Vertical Nanosheet, and Vertical Nanowire. The increase in electrostatic control and reduced short channel effects are key benefits to adopting GAAFET structures to meet scaling requirements for next generation process nodes. To understand channel geometry impacts on performance, the channel effective width (W eff ) is swept around the projected dimensions, including ratio of height and width parameters. The performance is evaluated using the key device metrics such as on-state current (I on ), off-state current (I off ), and threshold voltage (V t ) for transfer characteristics, and drain-induced barrier lowering (DIBL), subthreshold slope (SS), and gate induced drain leakage (GIDL) for short-channel effects. It is observed that thinner channel geometries, as often seen implemented in Nanosheet structures, have major benefits across SCE and I off metrics compared to more symmetrically square shaped channels. Additionally, stacking channels as a means to increase W eff appears to be an attractive option for increasing performance without significant increase in SCEs observed. For bulk technology the ratio between height and width of a Nanosheet structure can be optimized to reduce parasitic channel influence, so that optimal I on /I off ratio is achieved.

Latest articles

Been Kwak et al 2024 Eng. Res. Express 6 035335

In this study, high-density 3D-NAND flash memory is proposed. Using D-SSL, lateral shrink of cell area can be achieved by distinguishing two strings in a word-line (WL). We verify erase/program and read characteristics using technology computer-aided design (TCAD) simulations with rigorous calibrated condition. The proposed high density NAND flash has normally-on state SSLs through additional process and electrical treatment. In the conventional reference NAND flash, the cell string connected to the bit-line (BL) is distinguished by WL cut (WLC). On the other hand, in the proposed high density NAND flash, the cell string is selected by utilizing the normally-on state SSLs using trapped hole and doped arsenic at the intersection of SSL1 and SSL2. Compared with the conventional scheme, the proposed D-SSL exhibits almost same erase/program and read characteristics. Consequently, the proposed D-SSL scheme can increase memory density with reduced number of WLCs by distinguishing strings using D-SSL.

Tianqi Li et al 2024 Eng. Res. Express 6 035540

In hammer mill crushing, the speed change of the rotor hammer head will have an effect on the efficiency of crushing, and so on. This paper takes the hammer mill rotor as the research object and carries out a simulation study, establishes the material particle crushing model, and verifies the validity of the model. The discrete element method (DEM) is used to simulate the movement of the material inside the hammer mill after crushing, revealing that the working principle of the hammer mill is based on the form of particle movement, and to study the law of the particle collision fracture bond energy, compressive stress and particle collision impact energy of the hammer mill. Through comparison, the final working condition of the hammer mill is that at the rotor speed of 1600 r min −1 , the working efficiency of the whole equipment is the highest.

Monika and Suraj Prakash 2024 Eng. Res. Express 6 035334

Belachew A Demiss and Walied A Elsaigh 2024 Eng. Res. Express 6 032102

Construction duration estimation plays a pivotal role in project planning and management, yet it is often fraught with uncertainties that can lead to cost overruns and delays. To address these challenges, this review article proposes three advanced conceptual models leveraging hybrid deep learning architectures that combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) while considering construction delivery uncertainties. The first model introduces a Spatio-Temporal Attention CNN-RNN Hybrid Model with Probabilistic Uncertainty Modeling, which integrates attention mechanisms and probabilistic uncertainty modeling to provide accurate and probabilistic estimates of construction duration, offering insights into critical areas of uncertainty. The second model presents a Multi-Modal Graph CNN-RNN Hybrid Model with Bayesian Uncertainty Integration, which harnesses multi-modal data sources and graph representations to offer comprehensive estimates of construction duration while incorporating Bayesian uncertainty measures, facilitating informed decision-making and optimized resource allocation. Lastly, the third model introduces a Hierarchical Spatio-Temporal Transformer CNN-RNN Hybrid Model with Fuzzy Logic Uncertainty Handling, which addresses the inherent vagueness and imprecision in construction duration estimates by incorporating hierarchical spatio-temporal transformer architecture and fuzzy logic uncertainty handling, leading to more nuanced and adaptable project management practices. These advanced models represent significant advancements in addressing construction duration challenges, providing valuable insights and recommendations for future research and industry applications. Moreover, this review article critically examines the application of hybrid deep learning architectures, specifically the combination of CNNs RNNs, in predicting construction duration estimates at the preconstruction stage while considering uncertainties inherent in construction delivery systems.

Hare Karthik S et al 2024 Eng. Res. Express 6 035539

The requirement for lightweight materials that can survive increasingly severe conditions for aerospace components has fueled the research on magnesium alloys. The current work investigates the influence of hot rolling on the creep characteristics of the Mg-5Ag alloy that is fabricated through a selective laser melting process. The creep properties of parent material (PM) and hot-rolled specimens (HRA) are determined by indentation creep tests at 250 °C, 275 °C, and 300 °C. The microstructural evolution, phase transformation kinetics, and texture evolution were deduced using instrumental analysis, including SEM, EBSD, TEM, and XRD. The specimen PM features α -Mg grains with intermetallic Mg-Ag particles along grain boundaries and random grain orientations with low-angle boundaries. However, specimen HRA has a more fraction of Mg-Ag precipitates, a dominant (0001) grain orientation, and higher peak intensity from the (002) plane. The HRA specimen also demonstrates higher apparent activation energies, attributed to microstructural changes from hot rolling. The results show that the creep resistance of the hot rolled Mg-5Ag alloy is superior to as-fabricated Mg-5Ag alloy based on the creep velocity, stress exponent, and apparent activation energy.

Review articles

Rajamurugan G et al 2024 Eng. Res. Express 6 032501

The use of composite materials has expanded rapidly in recent years due to their improved performance and environmental friendliness, particularly in the fields of energy generation, automotive, and aerospace. The vehicle bonnet engine shield and automobile interiors were the focus of this study's investigation of the microstructural properties of ramie epoxy composite reinforced with aluminium and copper foil. Three different composite combinations (RAC1-RAC3) were created utilizing the hand layup method, each with a unique circular geometrical design constructed of copper and aluminium foil with a thickness of 0.03 mm. Tensile, flexural, impact, hardness, erosion, and wear rates were among the mechanical and tribological properties that were ascertained using ASTM test specimens. The findings show that in terms of hardness value (24 HV), tensile strength (58 MPa), impact strength (1.4 J), and flexural strength (93 MPa), the composite RAC3 (60 mm pitch staggered hole) performs mechanically better than other composites. According to erosion studies, erosion is also significantly less when it affects the surface that faces aluminium rather than copper. The highest frictional coefficient at maximum load was attained by the RAC3 composite sample.

Hethu Avinash Dasari and Rammohan A 2024 Eng. Res. Express 6 032302

Electric Vehicles (EVs) are a rapidly growing segment in India's automotive sector, with an expected 70% growth by 2030. Lithium-ion (Li-ion) rechargeable batteries are favoured because of their high efficiency in power and energy delivery, along with fast charging, long lifespan, low self-discharge, and environmental friendliness. However, as a crucial subsystem in EVs, batteries are susceptible to faults arising from various factors. Li-ion battery faults can be categorized as internal or external. Internal faults stem from over-charging, over-discharging, overheating, acceleration and degradation processes, short circuits, and thermal runaway. External faults are caused by sensor malfunctions, cooling system failures, and cell connection problems. A Battery Management System (BMS) plays an essential role in regulating battery operation, monitoring its health status, and implementing fault diagnostic techniques. Fault diagnostic algorithms running on the BMS enable early or post-fault detection and control measures to minimize the consequences of faults, thereby ensuring battery safety and reliability. This paper reviews various internal and external battery fault diagnosis methods. In addition to battery fault detection, this work conducts a comparative analysis of optimization techniques for fault diagnosis, including Fuzzy Clustering, Long Short-Term Memory, Support Vector Machines, and Particle Swarm Optimization.

Binbin Wang et al 2024 Eng. Res. Express 6 032202

Vortex optical communication employing orbital angular momentum (OAM) has been a hot research field in recent years. Thanks to the orthogonality of the OAM, several multiplexing and modulation techniques have been developed that can effectively improve communication capacity. However, to achieve this, accurate mode recognition in the OAM-based free-space optical (FSO) communication system is essential. Generally, perturbations in the free space link significantly affect the transmission efficiency and distort the helical phase-front of OAM beams, which will result in intermodal crosstalk and poses a critical challenge in the recognition of OAM modes. To date, artificial intelligence (AI) technologies have been widely applied to address the aforementioned bottleneck of insufficient accuracy of existing techniques for OAM mode detection. Therefore, a review paper that discusses the recent developments and challenges of the most widely used AI algorithms for OAM mode recognition schemes, i.e., feedforward neural network (FNN), convolutional neural network (CNN), and diffractive deep neural networks (D 2 NN) is urgently required. By elaborating on the principles of these algorithms and analyzing recent reports, encompassing both experimental and simulated results, we established their profound importance in enhancing the accuracy of OAM mode recognition. Moreover, this work provides an outlook on the recent trends in this newly developed field and the critical challenges faced in effectively using AI for improving the reliability of the OAM-based FSO communication system in near future.

Accepted manuscripts

BEHERA et al 

Manganese ore contains valuable oxides, such as manganese oxide (MnO2/Mn2O3/Mn3O4), iron oxide (Fe2O3) and silica (SiO2). In the present study, an attempt has been made to separate these oxides through pyrometallurgical processing routes for the extraction of ferromanganese and silicomanganese. Lean manganese ores were subjected to ball milling followed by carbothermic reduction at a temperature of 1200 °C for 2 hrs. The reduced ores were then subjected to wet magnetic separation to obtain the magnetic constituents in the reduced ores. It was observed that it is not possible to remove the silica content from the ore. It appeared that the reduced sample had oxides of iron, silicon and manganese after its wet magnetic separation. The magnetic constituents were subjected to metallothermic reduction through pure aluminium powders to recover valuable metals/alloys.

Liang et al 

This study proposes a finite-time disturbed observer-based prescriptive performance adaptive super twisted sliding mode trajectory tracking control scheme for quadrotor UAV, which fully considers the dynamic characteristics of the position and attitude loops. Based on the super-twisted sliding mode algorithm, adaptive control, finite-time theory, and prescribed performance control, this scheme aims to stabilize the tracking of a quadrotor UAV along a prescribed trajectory. This is achieved even when faced with challenges such as modeling uncertainty, external uncertainty, and complex interference environments. Firstly, the complexity of controller design is simplified by transforming the trajectory tracking problem into a command tracking problem for the position and attitude system loops. Secondly, the coupling between the channels and the disturbance effects are unified as the aggregate disturbance. To address the issue of the adverse impact of aggregate disturbance on system performance, a new finite-time disturbance observer is developed to estimate the overall disturbance. The observer can effectively eliminate the negative effects of the aggregate disturbance without considering the disturbed boundary conditions during the design process. This enhances the observer's adaptability in practical applications. Then, the disturbance estimation information and the adaptive super-twisted algorithm are used to construct the adaptive super-twisted sliding mode controllers for the position and attitude loops, respectively. Additionally, a prescribed performance control is designed to predefined the time for the position loop to effectively suppress the deleterious effects of interference. This ensures that the position system can quickly meet the prescribed tracking requirements. Finally, the stability of the designed trajectory tracking control system is demonstrated using the Lyapunov theorem. Numerical simulation results verify the effectiveness and superiority of the proposed controller.

Kumar et al 

Triply periodic minimal surface (TPMS) based lattices are extensively explored as a scaffold design for bone regeneration. TPMS maintains zero mean curvature at each point and offers a large surface area comparable to a trabecular bone. The best four TPMS minimal surfaces (IWP. Neovius, primitive, and F-RD) were selected, designed, and fabricated using acrylonitrile butadiene styrene (ABS) resin through the stereolithography (SLA) technique. The results indicate that small changes in unit cell dimensions do not significantly alter the structure topology, which ensures stress distribution within the lattice remains relatively uniform across different unit cell sizes when the porosity level is constant. The optimal unit cell size (2 to 5 mm) and porosity (70 to 80%) significantly affect the compressive strength and surface area to volume (SA/V) ratio due to a unique arrangement of the internal architecture of each TPMS unit cell. The lattice structure (formed by stacking unit cell) of unit cell size 2.11 mm with 70% porosity exhibited a maximum compressive strength of 39.8 MPa in IWP, followed by Neovius, primitive, and F-RD-based lattice structures. Moreover, the lattice showed more stability under compression force, minimized stress concentration compared to a unit cell, and exhibited distinct deformation patterns at different strain levels during compression.

Anand et al 

In this work, a multiband hybrid fractal antenna is developed for 5G Sub-6GHz and public safety bands. Proposed antenna design is designed, analyzed, and optimized using HFSS simulator. Radiating element of the proposed antenna consists of Hybrid Fractal shape which is designed using Koch and Hilbert curve fractal geometry. Hybrid Fractal Antenna (HFA) is designed on a Multilayer FR4 substrate which hasa dielectric constant of 4.4. Parametric analysis of the HFA is done to get the optimum results. Proposed HFA resonates at 2.64 GHz (2.34-2.80 GHz), 3.87, 4.54, 5.02, 5.35 GHz (3.73-5.55 GHz), and 6.42 GHz (5.70-6.72 GHz) with a gain of 4.2, 0.9, 2.9, 3.3, 4.6, and 3.7 dBi respectively. The proposed HFA is useful for 5G bands such as: N7 band, N38 band, N40 band, N41 band, N47 band, N53 band, N79 band, N90 band and N93 band, and also useful for public safety band (4.9 GHz). Prototype of the proposed HFA is fabricated and tested in lab for the verification of simulated results. The results show good performance in terms of reflection coefficient, gain, and bandwidth. Measured results are in good agreement with the simulated results.

MALI et al 

The increasing concerns about energy security and environmental sustainability have intensified the search for alternative fuels. This study investigates the performance and emission characteristics of a dual-fuel diesel engine utilizing purified biogas and diesel across various compression ratios. A single-cylinder, direct-injection, water-cooled, variable compression ratio diesel engine was adapted to operate in dual-fuel mode, running experiments at compression ratios of 12, 14, 16, 18, and 20 with a constant injection timing of 23° before top dead center under different load conditions. The study reveals a peak brake thermal efficiency of 33.25% at a compression ratio of 20, demonstrating the potential of biogas as a viable alternative fuel. Notably, while carbon monoxide and hydrocarbon emissions decreased with higher compression ratios, nitrogen oxide emissions increased, highlighting a trade-off in emission characteristics. This work contributes to the understanding of biogas utilization in diesel engines, offering insights into optimizing engine performance and emissions through compression ratio adjustments. The findings can inform future developments in sustainable energy solutions.

More Accepted manuscripts

Open access

Aman Satija et al 2024 Eng. Res. Express 6 035220

We have developed an inexpensive system for generating random voltage states (RVS) on a FPGA platform. This system can be used for controlling optoelectronic devices in a quantum-key-distribution (QKD) system. We use an all-digital operation at the FPGA layer to generate two uncorrelated Boolean bit strings. These bit strings are converted to RVS using a multiplexer and a voltage buffer in order to drive commercially available optoelectronic devices. A National Instruments (N.I) real-time IO (RIO) platform was used for FPGA implementation. The FPGA layer was coupled to the desktop layer for real-time monitoring and logging of the Boolean bit strings. We characterize the performance of the multiplexer and the buffer and describe how their engineering performance trades-off with the fidelity of RVS generation.

Andleeb Zahra et al 2024 Eng. Res. Express 6 035329

This paper presents a promising avenue of Radio Frequency (RF) biosensors for sensitive and real-time monitoring of creatinine detection. Knowing creatinine levels in the human body is related to its possible association with renal, muscular, and thyroid dysfunction. The detection was performed using an Inter-Digitated Capacitor (IDC) made of copper (Cu) metal over an FR4 substrate. To demonstrate our methodology, we have chosen Phosphate Buffer (PB) as our solvent for making the creatinine solutions of different concentrations. Moreover, Assayed Chemistry Control (ACC), a reference control consisting of human serum-based solutions has been mixed with the different concentrations of creatinine in a ratio of 1:9 to spike the creatinine value in the ACC solution. The sensor has been designed using a High-Frequency Structure Simulator (HFSS) tool with an operating frequency of 2.53 GHz. Then the design is fabricated over the FR4 printed circuit board (PCB) and tested using a Vector Network Analyzer (VNA). However, the sensitive area of the IDC is introduced to grade 4 Whatman filter paper for the Sample Under Test (SUT) handling unit. The main advantage of using Whatman filter paper is that the uniform spreading of liquid reduces experimental error, and less volume is required for testing the sample. The principal idea implemented in the biosensor design is to track the shift in the operating frequency in the presence of different concentrations of creatinine mix in ACC solution with Phosphate Buffer (PB) solution as a reference.

E Farneti et al 2024 Eng. Res. Express 6 035109

Masonry arch bridges constitute a fundamental part of the European transport network. Given their historical relevance and ongoing functional role, often under significantly higher load conditions than originally designed for, a reliable assessment of their load-bearing capacity is essential to understand whether they can guarantee adequate structural performance. To address this need, research efforts have focused on the development of computational methods capable of providing realistic simulations of the structural and collapse behavior of this kind of structures. In this context, the present paper aims to evaluate the application of the recently developed Applied Element Method (AEM) to masonry arch bridges, using the well-known Prestwood bridge (Staffordshire, UK) as a benchmark case study. The bridge was modeled using AEM and loaded until collapse simulating the actual conditions of the in situ test carried out in 1986. Results show consistency, in terms of bearing capacity and collapse mechanism, with the experimental data and previous studies that used other numerical approaches, proving the ability of the Applied Element Method to provide an accurate estimate of the collapse behavior of this kind of structures. AEM's ability to represent collapse mechanisms involving large displacements, at a reduced computational cost, is especially useful for the design of alert and monitoring systems for structures in a damaged or pre-collapse state.

K Ramakrishna Kini et al 2024 Eng. Res. Express 6 035007

Fault detection is vital in chemical engineering systems to maintain operational efficiency, product quality, and safety through timely identification and correction of deviations from expected behavior. Although partial least squares (PLS) has proven effective in monitoring due to its ability to handle highly correlated variables, traditional detection metrics of PLS may fail to identify small abnormal changes as they rely solely on recent observations. This paper integrates PLS modeling framework with Hellinger Distance (HD)-based fault detection index to overcome the limitations of conventional detection metrics. The utilization of HD is motivated by its sensitivity to quantifying any dissimilarity between distributions, which makes it well-suited for detecting small deviations in process behavior. The HD-based index will be computed between the residuals obtained from the model in the offline stage and the online stage. The HD metric involves careful inspection and comparison of the residuals, which enables it to capture the sensitive details in the data, thus, enhancing the detection of faults. For increased flexibility, kernel density estimation is employed to establish the reference threshold of the PLS-HD approach. The performance of this approach will be evaluated using data from simulated Continuous Stirred-Tank Heater (CSTH) and Continuous Stirred-Tank Reactor (CSTR) processes, by considering various fault types such as bias, freezing, and sensor drift faults. The results demonstrate the superior performance of the proposed PLS-HD approach compared to conventional PLS monitoring methods.

tugce ongen et al 2024 Eng. Res. Express

The pile method, applied in the foundational operations of construction to enhance soil bearing capacity, is utilized in locations where there is insufficient soil support. Its purpose is to reinforce the foundation and improve soil stability before constructing a structure. In this study, vibrations occurring during the application of Vibrex-type piles for foundation reinforcement in a construction project located in the Konak district of Izmir Province were measured at various distances from the measurement points. The study further explored the influence of pile application on soil stability, considering the geomechanical properties of the site's soil structure. The resulting analysis from the study's graph indicates that stress and vibration velocity remain at optimal levels, suggesting favorable working conditions.

ANAND PAI et al 2024 Eng. Res. Express

Automotive turbochargers, essential for enhancing intake air pressure and boosting torque in internal combustion engines, operate at exceptionally high rotational speeds of approximately 100,000 to 300,000 rpm. Despite the implementation of dual or triple air filtration systems to filter contaminants, neglected maintenance can lead to clogged filters, resulting in the ingestion of metallic filter mesh, and other small-sized objects from filter indicators, washers, and plastic components into the turbocharger assembly. The current study explores the impact of foreign particles on the mechanics of turbocompressor impeller blades in automotive turbochargers through a computational approach. A finite element model of the turbocompressor wheel was developed with suitable boundary conditions for the turbocompressor and the foreign particle. A Design of Experiments (DoE) approach was employed using a Taguchi L12 orthogonal array to optimize the multiple parameters during the foreign particle impact. The study considered two geometric shapes of the foreign particle (conical with unit aspect ratio and hemispherical), two sizes, and two rotational speeds ranging from 150,000 to 250,000 rpm. ANSYS Explicit Dynamics(R) software was utilized for the numerical simulations to simulate the mechanics of foreign particle entry and the resulting damage on compressor blades.

Sachin Mishra et al 2024 Eng. Res. Express 6 035325

Most countries have access to abundant water resources through rivers and canals. Utilizing this renewable resource, electricity can be generated in an environmentally friendly manner without causing pollution. In rapidly developing countries like India with the abundance of natural resources and diversities, the development of Hydropower is gaining in importance to meet the country's demand. This work discusses the different operating conditions that may occur in real time of the standalone hydro power generation system. In this work, various operating conditions are considered in terms of faults and disturbances that occur on the load side. These effects of faults and disturbances may be caused in the generating side. It takes into consideration some major events from the load side i.e. small disturbance, load addition, load rejection, large disturbance. In this work, the above-mentioned objectives are achieved by creating a model of a hydro power plant in MATLAB Simulink and keeping its operating environment same, simulate different scenarios related to load side, and study its effect on the generator and generating system. This is achieved by changing the load side for different conditions like introducing a small fault into the system, changing the load on a larger scale, etc The conditions that are introduced are simulated in a period of 10 s time frame. The reaction of the generating side from these conditions is recorded and plotted on parameters that can show the effect directly on the generator.

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تحسين كفاءه التوازن الحرارى لمستخدمى الفراغات السكنيه Alamera Kamal Elden Elfeky, elamira elfeky, Walaa Ahmed Nour, and Lobna Abdallah Agha

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Spatial Environmental Suitability for The Localization of Sanitary Waste Landfills in Egypt Using Environmental Information Systems: Applying to Safaga Qism Wessam Mostafa Emam and Dalia Mahmoud Hassan

Agility Opportunities in Construction Project Management “Exploring Opportunities in Construction Projects in integration with Waterfall Methodology.” Nevine G. Gado Dr. and Noha A, Elsayed Dr

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Chemical Kinetic Investigation: Exploring the Impact of Various Concentrations of HHO Gas with a 40% Biodiesel/Diesel Blend on HCCI Combustion Mai H. Aboubakr; Medhat Elkelawy Prof. Dr, Eng.; Hagar Alm-Eldin Bastawissi; and Ayman Refat Abd Elbar

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SSCM: Self-Secured Cloud Model in Irrigation System Tahani M. Allam Dr. Tahani Allam

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Heat and Mass Transfer Characteristics during Vacuum Drying of Wood mohamed salah Elmetwaly, Lotfy Hassan Rabie Saker, and Mohamed Sameh Salem

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Numerical Study for the Behavior of Stainless-Steel Sigma Columns Under Axial Compression Load Hayat H. Hegazy, Nashwa M. Yossef Prof, and Mostafa F. Hassanein

Securing Body Area Networks with Fingerprint Cryptography and Authentication in MANET Alaa M. Elbanaa, Roayat Ismail Abdelfatah, and Mohamed E. Nasr

3D Printing Technology: Challenges, And Potentials in Achieving Sustainable Buildings Sahar Mohamed Abd El rahman and Sobhy Ibrahim Esmail

Performance and enhancement evaluation of a solar still by using spraying technology mahmoud mohamed dewedar, Yasser A. F. EL-Samadony, Alsayed AlSaeed Mohamed, and Ahmed Ibrahim Abdo

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This paper is in the following e-collection/theme issue:

Published on 20.8.2024 in Vol 26 (2024)

The Use of Deep Learning and Machine Learning on Longitudinal Electronic Health Records for the Early Detection and Prevention of Diseases: Scoping Review

Authors of this article:

• Laura Swinckels 1, 2, 3, 4 , MSc   ; 
• Frank C Bennis 5, 6, 7 , PhD   ; 
• Kirsten A Ziesemer 8 , MSc   ; 
• Janneke F M Scheerman 2, 3 , PhD   ; 
• Harmen Bijwaard 3 , PhD   ; 
• Ander de Keijzer 4, 9 , PhD   ; 
• Josef Jan Bruers 1, 10 , PhD  

1 Department of Oral Public Health, Academic Centre for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit, Amsterdam, Netherlands

2 Department Oral Hygiene, Cluster Health, Sports and Welfare, Inholland University of Applied Sciences, Amsterdam, Netherlands

3 Medical Technology Research Group, Cluster Health, Sport and Welfare, Inholland University of Applied Sciences, Haarlem, Netherlands

4 Data Driven Smart Society Research Group, Faculty of Engineering, Design & Computing, Inholland University of Applied Sciences, Alkmaar, Netherlands

5 Quantitative Data Analytics Group, Department of Computer Science, Vrije Universiteit, Amsterdam, Netherlands

6 Department of Pediatrics, Emma Neuroscience Group, Emma Children's Hospital, Amsterdam UMC, Amsterdam, Netherlands

7 Amsterdam Reproduction and Development Research Institute, Amsterdam, Netherlands

8 Medical Library, University Library, Vrije Universiteit, Amsterdam, Netherlands

9 Applied Responsible Artificial Intelligence, Avans University of Applied Sciences, Breda, Netherlands

10 Royal Dutch Dental Association (KNMT), Utrecht, Netherlands

Corresponding Author:

Laura Swinckels, MSc

Department of Oral Public Health

Academic Centre for Dentistry Amsterdam (ACTA)

University of Amsterdam and Vrije Universiteit

Gustav Mahlerlaan 3004

Amsterdam, 1081 LA

Netherlands

Phone: 31 205980308

Email: [email protected]

Background: Electronic health records (EHRs) contain patients’ health information over time, including possible early indicators of disease. However, the increasing amount of data hinders clinicians from using them. There is accumulating evidence suggesting that machine learning (ML) and deep learning (DL) can assist clinicians in analyzing these large-scale EHRs, as algorithms thrive on high volumes of data. Although ML has become well developed, studies mainly focus on engineering but lack medical outcomes.

Objective: This study aims for a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of disease. The medical insights and clinical benefits that have been generated were investigated by reviewing applications in a variety of diseases.

Methods: This study was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A literature search was performed in 2022 in collaboration with a medical information specialist in the following databases: PubMed, Embase, Web of Science Core Collection (Clarivate Analytics), and IEEE Xplore Digital Library and computer science bibliography. Studies were eligible when longitudinal EHRs were used that aimed for the early detection of disease via ML in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening and selection and data extraction were performed independently by 2 researchers.

Results: In total, 20 studies were included, mainly published between 2018 and 2022. They showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders. Demographics, symptoms, procedures, laboratory test results, diagnoses, medications, and BMI were frequently used EHR data in basic recurrent neural network or long short-term memory techniques. By developing and comparing ML and DL models, medical insights such as a high diagnostic performance, an earlier detection, the most important predictors, and additional health indicators were obtained. A clinical benefit that has been evaluated positively was preliminary screening. If these models are applied in practice, patients might also benefit from personalized health care and prevention, with practical benefits such as workload reduction and policy insights.

Conclusions: Longitudinal EHRs proved to be helpful for support in health care. Current ML models on EHRs can support the detection of diseases in terms of accuracy and offer preliminary screening benefits. Regarding the prevention of diseases, ML and specifically DL models can accurately predict or detect diseases earlier than current clinical diagnoses. Adding personally responsible factors allows targeted prevention interventions. While ML models based on textual EHRs are still in the developmental stage, they have high potential to support clinicians and the health care system and improve patient outcomes.

Introduction

Digitizing meaningful health information has been proven to contribute to diagnostics. Electronic health records (EHRs) are a digital repository of patient data and contain retrospective, current, and prospective information supporting health care [ 1 ]. EHRs contain a wealth of clinical information about early symptoms of a disease and registries of medical treatments [ 2 ]. These can be textual or imaging data and include both unstructured clinical notes and structured, coded data. One important aspect of textual EHRs is that they may include risk and preventive factors and early signs before a disease manifests. Especially for patients with multiple visits, many possible indicators are gathered in EHRs, resulting in possible early indications of disease. Therefore, for a good risk assessment, clinicians need the patient’s health information, physical examinations, laboratory test results, and history [ 3 ] available in EHRs.

In the past 15 years, an explosion in the volume of data registered in EHR systems has occurred [ 4 ]. In 2012, the yearly increase in the volume of stored data was up to 150% for hospitals [ 5 ]. Not only the number of records continues to increase over time, but EHRs are also quite extensive because of large free texts [ 6 ]. Even though the completeness and correctness of EHRs have been found to be at a high level [ 7 ], the usability during medical visits lags behind due to this rising volume and variety of EHR data [ 8 ]. Consequently, it has even become an experienced usability issue for clinicians to review clinical results and health information from the past [ 9 ]. This is quite problematic as some clinicians spend, on average, 32.1% of their time on EHRs reviewing medical care and notes from the past [ 10 ]. The increasing EHR workload causes exhaustion and burnout among clinicians [ 11 ], negatively affecting the health care quality. This can result in diagnostic errors (missed, delayed, or incorrect diagnoses) because of missed signs [ 12 ] registered in the past. In 67.4% of the cases, missing the chief presenting symptoms in EHRs was the reason for missed diagnoses. Overall, meaningful health records have the potential to support risk assessment and early diagnosis, but the increasing amount of data hinder clinicians from using them to their full potential.

It is currently known that supportive tools can simplify complex diagnostic tasks and reduce potential diagnostic errors [ 13 ]. There is accumulating evidence suggesting that machine learning (ML) can assist clinicians in analyzing large-scale EHRs as they thrive on high volumes of data. ML is able to fit models specifically adapted to patterns in the data and, compared to traditional statistics, is able to handle multidimensional data [ 14 ]. Deep learning (DL) is a subdomain of ML that uses neural networks with multiple (hidden) layers, incorporating complex interactions between variables [ 15 ]. Examples of well-developed ML models are based on imaging data for disease detection [ 16 , 17 ] and textual EHRs of hospitalization or intensive care data for predicting disease progression or therapy success [ 18 ]. One of the most promising aspects of DL in the context of EHRs containing historical and present clinical data is the ability to incorporate temporality into the model, that is, to base possible risk assessments on hidden patterns over time in clinical parameters. Indeed, DL models have also proved to be more effective by incorporating temporal information (ie, longitudinally processed) rather than cross-sectional information only [ 19 ]. Although the techniques of many ML (including DL) models have proved to be effective on EHRs, their focus is often on the engineering of architectures and frameworks [ 20 ], but they lack medical outcomes.

It is a loss of information if ML developments remain unknown in health care because of the technical perspective of most authors. Especially given that artificial intelligence (AI) is a black box, it is important to clarify the clinical benefits and additional medical insights that can be achieved through these techniques. Therefore, the aim of this review was to perform a scoping review of the evidence on how the use of ML on longitudinal EHRs can support the early detection and prevention of diseases. A preliminary search was conducted, and no current or underway systematic or scoping reviews on the topic were identified. Only 1 review on longitudinal EHRs has been conducted [ 2 ], but it focused on methodologies. This study will contribute to what is already known by scoping the substantive medical insights that ML models yield. Given the aim of this study, the following research questions were addressed:

• Which diseases have been detected in longitudinal EHRs using ML techniques?
• What EHR data have been used by ML methods for the early detection and prevention of diseases?
• What medical insights are generated by developing and using ML models on longitudinal EHRs?
• What clinical benefits may be reached through the application of ML models on longitudinal EHRs?

The conduct and reporting of this scoping review adhere to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) statement [ 21 ]. A protocol has been registered in the Open Science Framework (DOI: NY2TE).

Eligibility Criteria

Articles were included if they reported on early detection for timely prevention of diseases by using ML on longitudinal EHRs; the full description of eligible participants, concept, context, and types of sources can be found in the protocol. Overall, studies were screened according to several criteria.

Studies must have a clear focus on health care instead of a technical focus (eg, the article must include disease-specific information and interpretation, preferably executed and written from a health care perspective, and reflect on health or related care outcomes). Studies with a dominant technical focus or an engineering challenge or those using non–real-world data were assumed to be ineligible for this review.

ML (including DL) should be aimed at predicting, detecting, or contributing to the risk assessment of diseases. Models aiming for data extraction, clustering, or patient selection for trials did not fit this concept. The purpose also affects the technique used.

The prediction target of ML must be (the onset of) a disease or a medical event. By using the International Classification of Diseases, 11th Revision [ 22 ], we ensured that the primary outcomes were a disease or related medical event (ie, the cause of morbidity or mortality). Thus, studies that predicted disease severity once diagnosed, success of treatment, adverse drug reactions, phenotypes, or events that were not the cause of morbidity or mortality and did not focus on timely prevention were beyond the scope of this research. If the outcome was mortality, these articles were excluded because it is always a consequence of a disease or medical event.

Essential Elements of ML

Studies must incorporate the essential elements of ML, such as training, testing, or validation steps. DL was assumed as a subdomain within ML and, therefore, was included as well.

According to the broadest definition of an EHR [ 1 ], data were assumed as EHR data if these contained information supporting continuing, efficient, and quality integrated health care or describing the health status of a patient regardless of the collecting database. Studies must use manually entered EHR data, including textual and numeric values. Both structured (numeric or coded) and unstructured (clinical notes) data were accepted as eligible EHR data. EHRs with solely imaging data (such as x-rays or electrocardiograms) were beyond the scope of this review. EHRs from animals were excluded.

Longitudinal

Studies must use EHRs over time registered at multiple visits (before registering a disease or medical event).

Studies were included if they were conducted in the context of disease prevention. Optimal prevention in health care settings can be reached when participants at risk or signs of a disease are detected as early as possible, and therefore, these studies were eligible in the context of secondary prevention. Secondary prevention emphasizes early disease detection in subclinical forms and seeks to prevent the onset of illness [ 23 ]. Studies conducted using data gathered in intensive care settings during a hospital admission or data gathered at the emergency department cannot be viewed in the context of disease prevention because only tertiary preventive measures can be taken to reduce the effects or severity of the established disease as it is too late to influence the onset of disease.

Sample Size

Because ML is data driven (instead of conventional models that are hypothesis driven), only predictions based on >1000 participants in total were considered eligible. This threshold is based on theory (eg, calculations for multivariable predictions of binary outcomes [ 24 ]) and practice (eg, the range of sample sizes for disease prediction models on EHRs seen in the literature).

Study Design

Only study designs with clinical, real-world data were considered. If secondary research, such as other reviews, met the aforementioned criteria, the reference list was considered depending on the research question. Conference papers were also considered because of the high quality of evidence in computer science.

After several preliminary searches, 5 bibliographic databases (PubMed, Embase, Web of Science Core Collection [Clarivate Analytics], IEEE Xplore Digital Library, and computer science bibliography) were searched for relevant literature from inception to April 28, 2022. Searches were devised in collaboration with a medical information specialist (KAZ). The following search terms, including synonyms, closely related words, and keywords, were used as index terms or free-text words: “neural network,” “electronic medical record,” and “prediction.” We used only search terms capturing specific ML techniques that are able to predict or classify. The search strategy was adapted for each included database or information source. The searches contained no methodological search filter or date or language restrictions that would limit results to specific study designs, dates, or languages. We searched computer science bibliography for conference proceedings and hand searched meeting abstracts. Duplicate articles were excluded using the R package ASYSD (R Foundation for Statistical Computing), an automated deduplication tool [ 25 ], followed by manual deduplication in EndNote (version X20.0.3; Clarivate Analytics) by the medical information specialist (KAZ). The full search strategy used for each database is detailed in Multimedia Appendix 1 .

Study Selection

Following the search, all identified citations were collated and uploaded into Rayyan (Rayyan Systems Inc) [ 26 ] and EndNote (version X7.8). In total, 2 reviewers (LS and FCB) independently screened all potentially relevant titles and abstracts for eligibility. If necessary, the full-text article was checked against the eligibility criteria. Differences in judgment were resolved through a consensus procedure. The full texts of the selected articles were obtained for further review. As the aim was not to search for “the best available” evidence but to identify and perform a scoping review of all evidence, a critical appraisal was not systematically carried out.

Data Extraction

Data were extracted from the papers included in the scoping review by 2 independent reviewers (LS and FCB) using a data extraction form developed in Microsoft Excel (Microsoft Corp). This form was composed based on full-text findings relevant to the research question and was discussed by the research team. The data extraction sheet captured details about study characteristics, health care discipline, generated medical insights, and clinical benefits for health care and the way EHRs were processed temporally. Multimedia Appendix 2 provides the list and definitions of all data items. This form was piloted using the first 5 articles and was revised and slightly adjusted during the process of extracting data. The extraction of ML techniques was modified to include the extraction of all techniques that were internally compared by appointing the central model and the comparison. Any disagreements between the reviewers were resolved through discussion with additional reviewers. Authors were contacted to request missing or additional data where required.

Synthesis of Results

Extracted data were synthesized into results by frequency counts of concepts and qualitative narratives. Study characteristics, detected diseases, and EHR variables were listed in tabular form. The content of these tables was sorted by disease outcomes according to the International Classification of Diseases, 11th Revision disease categories from the World Health Organization. For data concerning medical insights and clinical benefits, a qualitative content analysis was carried out according to the guidance for scoping review knowledge syntheses [ 27 , 28 ]. After each study’s key findings were extracted, these were classified into concepts (1-6) and described using a narrative summary. We decided to describe both similarities and exceptions of the generated results and potential impact.

Selection of Evidence

The literature search generated a total of 895 references. After removing duplicates of references that were selected from >1 database, 483 (54%) of the references remained. By screening titles and abstracts, 426 (88.2%) of the articles were excluded. Of the remaining 57 articles, 2 (4%) could not be retrieved because they contained unpublished work. In the second phase, 55 full texts were reviewed for eligibility, and ultimately, 20 (36%) articles were included. Reports were mostly excluded due to wrong data, a technical focus, the absence of a longitudinal aspect, or models based on N<1000. No additional studies were found by checking reference lists. After the final screening, most included articles (18/20, 90%) were found in PubMed. The flowchart of the search and selection process is presented in Figure 1 .

Characteristics of the Included Studies

Of the 20 included articles [ 29 - 48 ], 19 (95%) were published between 2018 and 2022, and 1 (5%) was published in 2016. The aim of these studies to develop an ML or DL model and examine whether it was able to detect the disease of interest in longitudinal EHRs. Detected diseases or related medical events were hepatocellular carcinoma [ 29 ], type 2 diabetes or prediabetes mellitus [ 30 , 31 ], mental health conditions [ 32 ], dementia [ 33 , 36 ], cognitive impairment [ 34 ], psychosis [ 35 ], heart failure [ 37 ], cardiac dysrhythmia [ 38 ], cardiovascular and cerebrovascular events [ 39 ], cardiovascular disease [ 40 ], knee osteoarthritis [ 41 ], kidney function decline [ 42 , 43 ], extreme preterm birth [ 44 ], opioid overdose [ 45 ], and suicide attempts [ 46 ]. One study proposed a health index [ 47 ] based on the prediction of 3 important health events, and another study predicted future disease in the next hospital visit [ 48 ]. Sample sizes ranged from thousands to millions. In total, 10% (2/20) of the studies used an external validation data set [ 35 , 39 ]. Table 1 shows the included studies and the detected diseases.

a EHR: electronic health record.

b HbA 1c : glycated hemoglobin.

c GP: general practitioner.

d DETECT: Dynamic Electronic Health Record Detection.

e KOA: knee osteoarthritis.

f APOL1-HR: apolipoprotein L1 high-risk.

g eGFR: estimated glomerular filtration rate.

The EHRs of patients used in the included studies were originally recorded in hospitals or primary care centers. Especially for the detection of mental and behavioral disorders, EHRs were often extracted from military health records [ 32 , 36 ], and for neurodevelopmental and cardiovascular disorders, EHRs were mostly extracted from general practices [ 33 , 37 ]. Most studies (16/20, 80%) used structured EHRs [ 29 - 33 , 35 , 38 - 43 , 45 - 48 ], sometimes combined with unstructured data [ 34 , 36 , 37 , 44 ], to estimate the risk of a disease or medical event. Demographic information (statically used), symptoms, laboratory (blood) test results, diagnoses, medications, BMI, and clinical notes were commonly used data from EHRs. In addition, the EHR length and hospital admission and visit history were frequently added to the model. Lifestyle data were included for cardiovascular diseases. Clinical and social signs were more frequently used for self-harm and mental, behavioral, and neurodevelopmental disorders. For the prediction of kidney and diabetes outcomes, laboratory test results were frequently extracted. If EHRs were unstructured, natural language processing methods were conducted as a precursor to analyze clinical notes. The central techniques were a basic recurrent neural network (RNN) or long short-term memory (LSTM) [ 29 , 31 , 34 , 35 , 39 , 44 , 45 , 49 ], often compared with logistic regression, support vector machine, or random forest. When techniques were used that could not handle temporal data, a temporal aspect was created in the data. Although not extensively specified, a slope and intercept of variables [ 31 , 36 ]; a mean [ 30 ]; minimum, maximum, median, and SD measures [ 42 ]; the addition of a time-weight (eg, 0.9 × days from reference point+decay) [ 43 ]; different time stamps [ 42 ]; or dividing the data into time blocks [ 33 , 46 ] were used. Multimedia Appendix 3 [ 29 - 48 ] provides an overview of the EHR data used and the techniques applied.

Medical Insights

Disease detection and prevention can be supported by using ML or DL on longitudinal EHRs. First, the development and training of such models on EHRs can generate new medical insights (1-4). Second, when those models are applied (eg, for additional analyses or to “new” data in clinical practice), the following clinical benefits may be achieved (5 and 6). These insights will be summarized in the following sections.

Medical Insight 1: Diagnostic Performance

The use of ML and DL models on EHRs could support the detection of diseases with a high diagnostic accuracy. Performance metrics such as the area under the receiver operating characteristic curve (AUROC), sensitivity (recall), specificity, accuracy, precision, and the area under the precision-recall curve evaluated the detecting ability of the model. The AUROC was by far the most frequently reported metric because it illustrates the diagnostic ability for a binary classification (disease or nondisease) by using the sensitivity versus the specificity. Although it is not our intention to identify the best-performing model, it was observed that the AUROC of central models varied between 0.73 and 0.97. In 40% (8/20) of the studies, the optimal model had a “good” detection (AUROC between 0.7 and 0.8), 35% (7/20) of the studies succeeded in having a “very good” detection (AUROC between 0.8 and 0.9), and 15% (3/20) of the studies reached an “excellent” detecting performance (AUROC between 0.9 and 1.0) [ 36 , 41 , 46 ] according to the classification of diagnostic accuracy by Simundic [ 50 ]. For the best disease detection, multiple models were compared within the study, or the central model was compared with existing detection tools. The authors of 30% (6/20) of the studies claimed that their model produced a (slightly) higher performance than “conventional” or “traditional” models or ML models in the literature [ 29 , 34 , 37 , 38 , 44 , 45 ]. In 15% (3/20) of the studies, the central model performed better compared with currently used approaches such as a validated clinical model [ 42 ], a surveillance tool on which current health indexes are based [ 47 ], and a gold standard in routine clinical practice according to the American College of Cardiology and the American Heart Association [ 40 ]. In one study, the prediction scores of the model were validated by experts who agreed 100% through manual record reviewing [ 36 ]. The diagnostic accuracy of the included models was not dependent on disease categories but relied on the EHR data given to the model. Many studies (7/20, 35%) mentioned that diseases could be detected more accurately (ie, the predictive performance was increased) when the EHRs were closer to the date of diagnosis [ 32 , 33 , 46 ] and with an increase in the number of predictors [ 37 , 40 , 43 , 48 ]. Overall, the ability of the included models to classify nonhealthy and healthy individuals was close to the registered diagnoses in the EHRs.

Medical Insight 2: Earlier Detection

In 45% (9/20) of the studies, ML and DL models observed all available EHR data to classify patients as a case or control (ie, ML vs human detection) [ 30 , 33 , 34 , 36 , 38 , 39 , 42 , 43 , 45 ]. However, in the other studies (10/20, 50%), models were able to detect diseases earlier than the moment they were diagnosed by clinicians in EHRs (ie, prediction) [ 29 , 31 , 32 , 35 , 37 , 40 , 41 , 44 , 46 - 48 ]. By dividing the participants’ EHRs into 2 pieces, X years were observed (observation period), and based on these data, it was possible to predict the risk of developing a disease or medical event in the future (prediction period). In other words, the prediction was made at an earlier time (x=0) than when it was diagnosed in practice (end of black bars). In some studies (5/20, 25%), it was part of the research to identify what time frame encompasses enough predictive information and, therefore, how much earlier an (accurate) detection was possible [ 32 , 33 , 37 , 43 , 46 ]. For example, Walsh et al [ 46 ] used 2 years of EHRs and extended their prediction window more and more to find the earliest moment of an accurate prediction. Raket et al [ 35 ] predicted whether a psychosis would occur 1 year before its onset, whereas Zhao et al [ 40 ] used 7 years of EHRs to predict the occurrence of cardiovascular events in the following 10 years. Figure 2 [ 29 - 48 ] illustrates the different time frames of longitudinal EHRs and their results according to a possible earlier detection. How much earlier a disease can be detected has a varying clinical meaning and, therefore, needs its own interpretation.

Medical Insight 3: Important Predictors

Another way to support disease detection and prevention was by generating insights into factors, topics, predictors, or indicators contributing to disease prediction [ 30 , 31 , 33 , 35 - 41 , 43 - 46 ]. In unstructured clinical notes, relevant topics, related words, and medical concepts were found that contributed to disease detection [ 36 , 44 ]. These words concerned daily living, behavior, and medical history. ML and DL models using structured EHRs generated the most contributing factors and their individual contribution to the outcome [ 30 , 31 , 33 , 35 , 37 - 41 , 43 , 45 , 46 ]. The most contributing predictors reported among all disease categories were (related to) age, blood pressure, BMI, cholesterol, smoking, and specific medication. Concerning mental, behavioral, and neurodevelopmental disorders, additional predictors were related to depression, personal difficulties, and personality changes. Some of these identified predictors were new for their discipline (eg, specific medication) [ 35 , 41 , 44 ] or not yet incorporated into gold standards for clinical diagnostic guidelines (eg, genetic information) [ 40 ]. In addition to this, insights into the importance of (known) predictors were generated. For example, Raket et al [ 35 ] identified what factors were responsible for the biggest positive and negative change in risk estimation (eg, differential white blood cells) and, therefore, indicated the most effective targets for preventive interventions. Other models found that the contribution of some predictors was not as high as assumed (eg, stress on diabetes) [ 31 ]; factors that seemed individually irrelevant turned out to have cumulative important predictive value [ 35 ], and the instability of factors, not the factor itself, was a predictor for one disease [ 40 ]. The aforementioned factors were identified during model development, but applying such a model to new EHRs would generate responsible factors for that individual.

Medical Insight 4: Other Health Care Indicators

In total, 10% (2/20) of the studies used EHRs not to predict the risk of a disease but to create other health indicators. Hung et al [ 47 ] developed a health index based on 3 DL predictions of impactful and costly health indicators (mortality, hospitalization, and cancer). This health index also generated insights into the population’s health and was found to be close to the “true risk” and, therefore, a better indicator than baseline models. Another study claimed to forecast what disease an individual would have at the next hospital visit [ 48 ]. Their results showed that the developed model generated well-performing results in forecasting medical diagnoses aggregated in 3- and 4-digit International Classification of Diseases, 9th and 10 th Revision codes.

Clinical Benefits

Clinical benefit 5: preliminary screening.

In 25% (5/20) of the studies, ML models were used to support (preliminary) screening on longitudinal EHRs [ 29 , 35 , 36 , 42 , 46 ]. After developing ML and DL models, risk classes could be generated as a precursor for physical screening. Approximately 90% of the diagnosed cases were concentrated in the highest (10%) risk class. Other studies assessed the utility of ML and DL models by thresholds for the proportion needed to be screened versus the detection possibility [ 29 , 42 ]. For example, to detect 90% of all validated patients with hepatocellular carcinoma, the highest 66% of risk scores (predicted by a DL model) needed to be screened, whereas to detect 80% of all cases, screening from only the highest 51% of risk scores was required [ 29 ]. Chauhan et al [ 42 ] reasoned the other way around and focused on efficiency. From the 10% highest risk scores for kidney failure, the positive predictive value was 68%. Moreover, the cost benefits for screening options using DL on EHRs were investigated [ 35 ]. Disease detection using a DL model was associated with a positive net benefit–to–cost benefit ratio for a single-point risk assessment (1:3) and continuous-time risk assessment (1:16). Reasons for preliminary screening in EHRs were to prioritize those with the highest risk for disciplines with long waiting lists [ 29 , 42 ], before costly or more invasive examinations (eg, image or biomechanical retrieval) [ 35 , 41 ], or to detect cases that might be missed by the current pathway and go undetected [ 35 , 36 , 46 ].

Clinical Benefit 6: Possible Clinical Benefits

Only 10% (2/20) of the included studies were validated using an external data set, but none of the models have been implemented in clinical practice (yet). Consequently, the benefits for health were not evaluated. However, the authors interpreted their findings and suggested opportunities and possible health care benefits for clinical practice. The authors of 35% (7/20) of the studies mentioned that, if their models were applied in clinical practice, this may improve personalized health care [ 34 - 36 , 42 , 45 - 47 ]. Personalized health care was related to a personalized risk prediction, an individual-level index or output, a tailored care plan, and targeted care and screening. The authors of 60% (12/20) of the studies claimed that prevention could be improved by using their ML and DL models [ 31 - 38 , 42 , 44 , 45 , 47 ]. Early and timely detection and interventions before disease manifestation were often mentioned. In one case, the use of DL on EHRs could not directly prevent the targeted outcome, but by better preparing health care in an appropriate setting, indirect health outcomes could be prevented [ 44 ]. Additional suggestions to improve health care were focused on policies. It was suggested to base health policies on risk classes at a nationwide level [ 39 , 42 ]. Moreover, (predicted) future health conditions may be a better base for health care policies than traditional surveillance models reflecting health conditions from years before [ 47 ]. In addition to this, DL support can reduce the clinical workload. Even if the positive predictive value to select a screening population is low, a model with an excellent sensitivity can reduce the clinician’s workload by 70% [ 44 ]. All studies assumed EHR data to be valuable information to improve health care. The author of one study suggested that even imperfect data can be used as a silver standard to develop risk models [ 36 ].

Summary of Evidence

The first research question in this study sought to determine which diseases have been detected in longitudinal EHRs using ML techniques. Results showed that a variety of diseases could be detected or predicted, particularly diabetes; kidney diseases; diseases of the circulatory system; and mental, behavioral, and neurodevelopmental disorders [ 22 ]. Comparing our findings with those of prior work, only a third of EHR prediction models predict diseases; meanwhile, mortality and hospitalization remain the most prevalent outcomes [ 51 ]. Among the studies that have predicted diseases, cancer is the most frequently predicted disease based on EHRs. Another systematic review used clinical notes to identify chronic diseases [ 52 ]. It also found diseases of the circulatory system as the most prevalent and explained this by the structure of the data. Not only the structure but also the length of the EHR horizon before diagnosis may explain the diseases that can be detected or predicted. As we determined the scope of diseases that may be prevented, the length of historic data before the diagnosis (in existence of early signs) reflects the “preventive stage” before the onset of the disease. The literature confirms that the longest EHR time horizon (8-10 years) has been found for diabetes and cardiovascular and kidney diseases [ 51 ], which were also prevalent diseases in our scoping review. In the end, the diseases that can be detected rely on available EHR data and, therefore, previous medical visits.

The second research question determined the scope of what EHR data have been used by ML techniques for the early detection and prevention of diseases. This scoping review found that age, sex, BMI, symptoms, procedures, laboratory test results, diagnoses, medications, and clinical notes are frequently used. Diseases that could be detected earlier than when they are currently diagnosed did not use other EHR variables. In addition, the most important predictors found in multiple studies were age, blood pressure, BMI, cholesterol, smoking, and medication. The consistency in the used and most important EHR variables underlines the importance of establishing generalized regulation and standardization of these variables across electronic health software, especially for variables overlapping in various health disciplines [ 53 ]. This would also address well-known challenges and limitations with EHR data, which will be discussed later in this section. According to the literature on the use of EHR data, it seems that a larger variable set improves disease prediction [ 51 ]. Their systematic review concluded that studies must leverage the full breadth of EHR data by using longitudinal data. In addition, we found that large longitudinal EHR data can successfully be analyzed via RNN and, derived from it, LSTM. These are both neural network architectures that are able to find patterns while incorporating temporality, making them effective for time-series predictions. Other types of neural networks (eg, convolutional neural networks) are well-known for their performance on images [ 15 ]. Similar results for techniques were identified in a review on the same topic from a technical perspective [ 2 ]. They concluded that RNN (specifically LSTM) was the most prominent technique to capture complex time-varying EHRs. Another review on AI techniques to facilitate earlier diagnoses of cancer also stated that neural networks were the dominant technique applied to EHRs [ 54 ]. Our results showed that there was no consistent way to process EHR variables temporally when techniques other than LSTM and RNN were used. Therefore, we can conclude that a basic RNN and LSTM are the most suitable techniques to analyze multivariable, longitudinal EHRs.

The third research question of this review was to determine the scope of medical insights that could be generated. Our results showed that, with the development and training of ML and DL models on EHRs, (1) a high diagnostic accuracy was reached, (2) the most responsible predictors could be identified, (3) diseases could be detected earlier than when they are currently diagnosed, and (4) additional health care indicators were created. The most prominent medical insight was the detection performance of the models. However, how good the performance should be is ambiguous. For example, DL models used to facilitate earlier cancer diagnoses had AUROC values ranging from 0.55 to 0.99 [ 54 ], indicating performance from almost random guessing to near-perfect detection. Looking into a more mature domain, the diagnostic accuracy of sepsis predictions ranged from between 0.68 and 0.99 in the intensive care unit to between 0.96 and 0.98 in hospital and between 0.87 and 0.97 in the emergency department [ 55 ]. This metric is ideally as high as possible because it induces a high sensitivity (true positives) and specificity (true negatives). For comparison, the diagnostic accuracy of a gut feeling (meta-analysis on cancer diagnosis) had a sensitivity of only 0.40 and a specificity of 0.85 [ 56 ]. The diagnostic accuracy of physical examination (for the detection of cirrhosis) had a sensitivity between 0.15 and 0.68 and a specificity between 0.75 and 0.98 [ 57 ]. If ML can increase both the sensitivity and specificity of disease detection, nonhealthy persons can be found, and delayed diagnoses can be reduced without overtreating healthy persons misdiagnosed as cases [ 58 ]. If the developed model is further evaluated in false-negative and false-positive groups, it may be possible that the model detects even more (true) cases than those registered by clinicians. This is already the case for many DL techniques on imaging data [ 59 ]. For now, an even more important finding is the ability of some models to detect disease manifestation earlier than the moment of diagnosis registration in EHRs. These examples of earlier detection are aligned with a study on the onset of diseases [ 60 ] that concluded that “slowly progressive diseases are often misperceived as relatively new” (ie, the onset could have been detected earlier). They found that, in 31% of diagnosed cases, the onset of their disease had started >1 year before their diagnosis. When disease predictions are early and accurate enough, it can facilitate disease prevention [ 23 ]. Especially with the addition of personally responsible factors and the biggest changers in risk prediction, prevention interventions may be more effective because they are more targeted to the individual. When medical prevention and interventions become based on the unique profile of each individual, personalized health care is delivered [ 61 ]. After all, the aforementioned medical insights only show the bright side of ML and DL models.

Our final research question sought the (possible) clinical benefits that could be obtained from using ML on EHRs. We found that preliminary screening was a clinical benefit of applying such models on longitudinal EHRs. Patients were accurately classified into risk classes to prioritize those with the highest risk, and a positive net benefit was found. In addition, the authors of the studies stated that their results (although they were not clinically evaluated) may contribute to a more personalized health care, prevention possibilities, and health care policies and reduce the clinicians’ workload. These benefits are perfectly aligned with the near-future vision, strategies, and action foci set by the World Health Organization [ 62 , 63 ]. In particular, the emerging clinical staff shortage makes the future health care system more dependent on technical innovations and the health care system will be forced to be digitally assisted [ 64 ]. However, to be adopted in medical practice, ML and DL models require external validation, the absence of bias and drift, and transparency for clinicians. In prior work, benefits have rarely been clinically evaluated either. Even in a more mature health domain regarding ML, the intensive care unit, only 2% of the AI applications are clinically evaluated [ 65 ]. In their systematic review, the clinical readiness of AI was explored, but no AI model was found to be integrated into routine clinical practice at the time of writing. The limited amount of publications evaluating the clinical benefits of the application of ML on EHRs indicates the research gap in the literature. Future studies should explore the follow-up of these AI attempts and the reasons for success or failure in practice.

Up until now, we have only discussed possible beneficial results of using ML and DL on EHRs. However, we cannot ignore the possible risks, obstacles, challenges, or issues. Multiple (systematic) reviews have summarized these well-known issues, challenges, and limitations regarding the application of ML and DL on EHRs [ 2 , 51 , 66 , 67 ]. Viewed generally across all studies, practical obstacles influence the scientific and clinical implementation process: ethical considerations, privacy guidelines, legal procedures, equity, and data protection and security [ 68 ]. Beyond these obstacles, existing predictions face limitations due to their reliance on the data. First, key issues of using EHRs are irregularity, heterogeneity, sparsity (eg, missing data), temporality, the lack of gold-standard labels, and the volume and quality of data [ 2 , 51 , 66 , 67 ]. Second, ML and DL models have limited transparency and interpretability, face domain complexity (vs engineering expertise), may include biases, and often lack external validation. It is not possible to assign specific issues to specific studies; they all suffer more or less from the aforementioned issues. Our point is to become aware of the downside as well. Therefore, all our principal findings must be interpreted with this last discussion point in mind. In our opinion, a consistent, reliable, and valid way of EHR registration will improve the (use of) data and could be the first step toward a data-based health care system. This need for movement and improvement is important not only for research but also for practical convenience for clinicians and, consequently, to succeed in improving health outcomes.

Limitations

A limitation of this scoping review is the time between the search and the publication. As ML and DL have become a popular topic and the amount of research has grown drastically over the last years, new research could have been published between the literature search and the publishing of this scoping review. Consequently, some of our findings may have been overtaken by the progress in research.

Another limitation was the data synthesis regarding the performance outcomes per technique. Due to a wide variety of internal analyses, outcomes were not directly comparable, and therefore, the data extraction and data synthesis were difficult. Some studies just noted the optimal performance value achieved by the central model, while other studies compared a variety of techniques and noted various performance values for different subgroups, different metrics, and different time windows and with the addition of various technical improvements. A few authors discussed their ultimate results and mentioned that their model was better than literature, that is, “traditional” or “conventional,” attempts, which were not always clearly defined. We have attempted to follow the authors’ description to avoid incorrect comparisons. However, some comparisons may have become vague or skewed during data synthesis. Nevertheless, we scoped the optimal AUROC for each study at the meta level.

As we used a broad definition of EHR, we included a greater range of data. This means that the results are not based solely on data directly extracted from clinical record systems but also on data extracted by an intermediate organization, such as insurance companies. Therefore, readers must interpret the results of ML and DL models with this in mind.

Conclusions

Longitudinal EHRs have valuable potential to support the early detection of a variety of diseases. For various diseases, EHR data concerning diagnoses, procedures, vital signs, medication, laboratory tests, BMI, and (early) symptoms have a high predictive value. To analyze multivariable, longitudinal EHRs, a basic RNN and LSTM are the most suitable techniques. For the detection of diseases, using ML (including DL) on EHRs proved to be highly accurate. When the detection occurs at the same moment as the diagnosis of clinicians, it seems not directly relevant for the prevention of diseases. However, the detection of diseases offers the clinical benefits of preliminary screening to prioritize patients from the highest risk class. The prevention of diseases can be supported by ML models that are able to predict or detect diseases earlier than the current clinical practice. The additional information about the most important predictors of the individual and the biggest risk changers allow targeted prevention interventions and, therefore, personalized care. Improved health care policies and workload reduction are frequently cited benefits but have not yet been evaluated in clinical practice. Both ML and DL attempts for disease detection and prevention still remain in the testing and prototyping phase and have a long way to go to be clinically applied.

Acknowledgments

The first author conducted this study as part of her PhD trajectory. Her PhD trajectory was funded by the Centre of Expertise Prevention in Care and Wellbeing from Inholland University of Applied Sciences. JS acknowledges financial support from Regieorgaan SIA RAAK, part of the Netherlands Organisation for Scientific Research (grant HBOPD.2018.05.016). The remaining authors declare no other external sources of funding for this scoping review.

Authors' Contributions

All the authors made substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data. LS and FCB screened, extracted, analyzed, and interpreted the data. KAZ designed the search strategy and ran, exported, and deduplicated the search results. All authors revised the paper critically and have granted final approval for the version to be published.

Conflicts of Interest

None declared.

Search strategy.

Data extraction instrument.

Electronic health record data and applied techniques.

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

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Abbreviations

Edited by T de Azevedo Cardoso, S Ma; submitted 19.04.23; peer-reviewed by J Zeng, V Rajan, D Chrimes; comments to author 11.07.23; revised version received 29.09.23; accepted 29.04.24; published 20.08.24.

©Laura Swinckels, Frank C Bennis, Kirsten A Ziesemer, Janneke F M Scheerman, Harmen Bijwaard, Ander de Keijzer, Josef Jan Bruers. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.

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Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors

Cancer Discov 2024;XX:1–13

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Ju-Fang Chang , Jack H. Landmann , Tien-Ching Chang , Mehmet Emrah Selli , Yangdon Tenzin , John M. Warrington , Julie Ritchey , Yu-Sung Hsu , Michael Slade , Deepesh Kumar Gupta , John F. DiPersio , Alex S. Holehouse , Nathan Singh; Rational Protein Engineering to Enhance MHC-Independent T-cell Receptors. Cancer Discov 2024; https://doi.org/10.1158/2159-8290.CD-23-1393

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Chimeric antigen receptor (CAR)–based therapies have pioneered synthetic cellular immunity but remain limited in their long-term efficacy. Emerging data suggest that dysregulated CAR-driven T-cell activation causes T-cell dysfunction and therapeutic failure. To re-engage the precision of the endogenous T-cell response, we designed MHC-independent T-cell receptors (miTCR) by linking antibody variable domains to T-cell receptor constant chains. Using predictive modeling, we observed that this standard “cut and paste” approach to synthetic protein design resulted in myriad biochemical conflicts at the hybrid variable–constant domain interface. Through iterative modeling and sequence modifications, we developed structure-enhanced miTCRs which significantly improved receptor-driven T-cell function across multiple tumor models. We found that 41BB costimulation specifically prolonged miTCR T-cell persistence and enabled improved leukemic control in vivo compared with classic CAR T cells. Collectively, we have identified core features of hybrid receptor structure responsible for regulating function.

Significance: Improving the durability of engineered T-cell immunotherapies is critical to enhancing efficacy. We used a structure-informed design to evolve improved miTCR function across several models. This work underscores the central role of synthetic receptor structure in T-cell function and provides a framework for improved receptor engineering.

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