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arXiv 2023年

作者： Alam, Ridwan Aguirre, Aaron D. Stultz, Collin M. Research Laboratory of Electronics MIT CambridgeMA United States Computer Science & Artificial Intelligence Laboratory MIT CambridgeMA United States Department of Electrical Engineering and Computer Science MIT CambridgeMA United States Wellman Center for Photomedicine Massachusetts General Hospital BostonMA United States Division of Cardiology Massachusetts General Hospital BostonMA United States Harvard Medical School BostonMA United States Harvard-MIT Program in Health Sciences and Technology CambridgeMA United States Institute for Medical Engineering and Science MIT CambridgeMA United States

Background and Aims For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We aim to develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and we use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Methods Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. Results QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Conclusions Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning. This research leads the path toward out-of-hospital care using wearable ECG devices for antiarrhythmic therapies. © 2023, CC BY-NC-ND.

关键词： Electrocardiograms

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Parameter-free differential evolution algorithm for the analytic continuation of imaginary time correlation functions

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Physical Review E 2022年第2期106卷 025312-025312页

作者： Nathan S. Nichols Paul Sokol Adrian Del Maestro Data Science and Learning Division Argonne National Laboratory Argonne Illinois 60439 USA Department of Physics University of Vermont Burlington Vermont 05405 USA Materials Science Program University of Vermont Burlington Vermont 05404 USA Department of Physics Indiana University Bloomington Indiana 47408 USA Department of Physics and Astronomy University of Tennessee Knoxville Tennessee 37996 USA Min H. Kao Department of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee 37996 USA

We report on differential evolution for analytic continuation: a parameter-free evolutionary algorithm to generate the dynamic structure factor from imaginary time correlation functions. Our approach to this long-standing problem in quantum many-body physics achieves enhanced spectral fidelity while using fewer compute (CPU) hours. The need for fine-tuning of algorithmic control parameters is eliminated by embedding them within the genome to be optimized for this evolutionary computation-based algorithm. Benchmarks are presented for models where the dynamic structure factor is known exactly and experimentally relevant results are included for quantum Monte Carlo simulations of bulk He4 below the superfluid transition temperature.

关键词： Nonequilibrium statistical mechanics Helium-4 superfluids Genetic algorithm Neutron scattering Numerical techniques Path-integral Monte Carlo Quantum Monte Carlo

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Concealed drug detection via fan beam coded-aperture X-ray diffraction imaging 6

Concealed drug detection via fan beam coded-aperture X-ray d...

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Anomaly Detection and Imaging with X-Rays (ADIX) VI 2021

作者： Greenberg, Joel A. Stryker, Stefan Carpenter, Josh Kapadia, Anuj J. Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Medical Physics Graduate Program Duke University DurhamNC27708 United States Carl E. Ravin Advanced Imaging Laboratories Dept. of Radiology Duke University DurhamNC27708 United States Quadridox Inc. HillsboroughNC27278 United States

ISBN: (数字)9781510643147

ISBN: (纸本)9781510643130

Detecting illegal drugs in packages without opening the package is a challenge. X-ray diffraction tomography offers the potential capability of uniquely identifying illegal drugs within a package based on their atomic structure. To understand the potential for XRD-based drug detection, we developed a method for building a library of X-ray diffraction drug signatures. In addition, we used a compact, multi-modality fan beam X-ray imaging system (combining transmission and X-ray diffraction) to distinguish oxycodone from non-prescription medicine when concealed within different types of containment. © 2021 SPIE.

关键词： X ray diffraction

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A Unified Joint Optimization of Training Sequences and Transceivers Based on Matrix-Monotonic Optimization

arXiv

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arXiv 2022年

作者： Xing, Chengwen Yu, Tao Song, Jinpeng Zheng, Zhong Zhao, Lian Hanzo, Lajos School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of Cyberspace Science and Technology Beijing Institute of technology Beijing100081 China The Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada The Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Channel estimation and data transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions. Copyright © 2022, The Authors. All rights reserved.

关键词： Matrix algebra

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Autonomic Dysfunction in REM Sleep Disorder: The Role of Respiration in HRV Analysis*

Autonomic Dysfunction in REM Sleep Disorder: The Role of Res...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Parisa Sattar Nicla Mandas Giulia Baldazzi Elisa Facchini Elisa Casaglia Michela Figorilli Laura Giorgetti Pietro Mattioli Francesco Calizzano Francesco Fama Dario Arnaldi Monica Puligheddu Danilo Pani Pablo Laguna Raquel Bailon Department of Biomedical Sciences Neuroscience PhD Program University of Cagliari Cagliari Italy Department of Electrical and Electronic Engineering MeDSP Lab University of Cagliari Cagliari Italy The Hadron Academy Istituto Universitario di Studi Superiori IUSS Pavia Italy Interdepartmental Sleep Disorder Research Center University of Cagliari Cagliari Italy SC Neurology AziendaOspedaliero-Universitaria Cagliari Italy Department of Neuroscience Rehabilitation Ophthalmology Genetics Maternal and Child Health (DINOGMI) Clinical Neurology University of Genoa Genoa Italy Neurophysiopathology Unit IRCCS Ospedale Policlinico San Martino Genoa Italy Biomedical Signal Interpretation and Computational Simulation (BSICoS) Aragón Institute for Engineering Research (I3A) IIS Aragón University of Zaragoza Spain Zaragoza Spain Centro de Investigacion Biomedica en Red en Bioingeniería Biomateriales y Nanomedicina (CIBER-BBN) Madrid Spain

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Autonomic nervous system (ANS) has been suggested often associated with the isolated rapid eye movement (REM) sleep behavior disorder (iRBD), with iRBD, and heart rate variability (HRV) is a commonly used tool to assess it. However, the accuracy of HRV is heavily influenced by respiratory rate (RespR), potentially causing misinterpretation of frequency domain HRV indices. This study compares sleep-related ANS function between iRBD patients and a control group (CG) using respiratory-adjusted HRV spectral analysis. It involves 20 iRBD and 20 CG participants in each group, from which five-minute ECG signal epochs during N2, N3, and REM sleep phases are analyzed. RespR were estimated from ECG-derived respiratory signals. Both low and high frequency bands definition for HRV analysis were redefined using the RespR. The results indicate significant differences in HRV parameters between iRBD and CG subjects during REM sleep, highlighting disrupted autonomic regulation in iRBD. This finding is also evident in non-REM phases, thus emphasizing the need to incorporate RespR in HRV analysis, especially in sleep-related studies, for a comprehensive understanding of autonomic dysfunction.

关键词： Phase modulation Frequency-domain analysis Electrocardiography Regulation Rapid eye movement sleep High frequency Heart rate variability engineering in medicine and biology Spectral analysis Parasympathetic nervous system

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Transclaw U-net: Claw U-net with transformers for medical image segmentation

arXiv

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arXiv 2021年

作者： Yao, Chang Hu, Menghan Zhai, Guangtao Zhang, Xiao-Ping Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Institute of Image Communication and Information Processing Shanghai Jiao Tong University Department of Electrical Computer and Biomedical Engineering Ryerson University

In recent years, computer-aided diagnosis has become an increasingly popular topic. Methods based on convolutional neural networks have achieved good performance in medical image segmentation and classification. Due to the limitations of the convolution operation, the long-term spatial features are often not accurately obtained. Hence, we propose a TransClaw U-Net network structure, which combines the convolution operation with the transformer operation in the encoding part. The convolution part is applied for extracting the shallow spatial features to facilitate the recovery of the image resolution after upsampling. The transformer part is used to encode the patches, and the self-attention mechanism is used to obtain global information between sequences. The decoding part retains the bottom upsampling structure for better detail segmentation performance. The experimental results on Synapse Multi-organ Segmentation Datasets show that the performance of TransClaw U-Net is better than other network structures. The ablation experiments also prove the generalization performance of TransClaw U-Net. © 2021, CC BY.

关键词： Convolution

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Latent Signal Models: Learning Compact Representations of Signal Evolution for Improved Time-Resolved, Multi-contrast MRI

arXiv

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arXiv 2022年

作者： Arefeen, Yamin Xu, Junshen Zhang, Molin Dong, Zijing Wang, Fuyixue White, Jacob Bilgic, Berkin Adalsteinsson, Elfar Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Athinoula A. Martinos Center for Biomedical Imaging CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Harvard-MIT Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Institute for Medical Engineering and Science Massachusetts Institute of Technology CambridgeMA United States

Purpose: Training auto-encoders on simulated signal evolution and inserting the decoder into the forward model improves reconstructions through more compact, Bloch-equation-based representations of signal in comparison to linear subspaces. Methods: Building on model-based nonlinear and linear subspace techniques that enable reconstruction of signal dynamics, we train auto-encoders on dictionaries of simulated signal evolution to learn more compact, non-linear, latent representations. The proposed Latent Signal Model framework inserts the decoder portion of the auto-encoder into the forward model and directly reconstructs the latent representation. Latent Signal Models essentially serve as a proxy for fast and feasible differentiation through the Bloch-equations used to simulate signal. This work performs experiments in the context of T2-shuffling, gradient echo EPTI, and MPRAGE-shuffling. We compare how efficiently auto-encoders represent signal evolution in comparison to linear subspaces. Simulation and in-vivo experiments then evaluate if reducing degrees of freedom by incorporating our proxy for the Bloch-equations, the decoder portion of the auto-encoder, into the forward model improves reconstructions in comparison to subspace constraints. Results: An auto-encoder with one real latent variable represents FSE, EPTI, and MPRAGE signal evolution as well as linear subspaces characterized by four basis vectors. In simulated/in-vivo T2-shuffling and in-vivo EPTI experiments, the proposed framework achieves consistent quantitative NRMSE and qualitative improvement over linear approaches. From qualitative evaluation, the proposed approach yields images with reduced blurring and noise amplification in MPRAGE shuffling experiments. Conclusion: Directly solving for non-linear latent representations of signal evolution improves time-resolved MRI reconstructions through reduced degrees of freedom. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Quantum Atomic Matter Near Two-Dimensional Materials in Microgravity

arXiv

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arXiv 2023年

作者： Maestro, Adrian Del Kim, Sang Wook Bigelow, Nicholas P. Thompson, Robert J. Kotov, Valeri N. Department of Physics and Astronomy Min H. Kao Department of Electrical Engineering and Computer Science Institute for Advanced Materials and Manufacturing University of Tennessee KnoxvilleTN37996 United States Department of Physics and Materials Science Program University of Vermont BurlingtonVT05405 United States Department of Physics and Astronomy Institute of Optics Center for Coherence and Quantum Optics University of Rochester RochesterNY14627 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Novel two-dimensional (2D) atomically flat materials, such as graphene and transition-metal dichalcogenides, exhibit unconventional Dirac electronic spectra. We propose to effectively engineer their interactions with cold atoms in microgravity, leading to a synergy between complex electronic and atomic collective quantum phases and phenomena. Dirac materials are susceptible to manipulation and quantum engineering via changes in their electronic properties by application of strain, doping with carriers, adjustment of their dielectric environment, etc. Consequently the interaction of atoms with such materials, namely the van der Waals / Casimir-Polder interaction, can be effectively manipulated, leading to the potential observation of physical effects such as Quantum Reflection off atomically thin materials and confined Bose-Einstein Condensate (BEC) frequency shifts. Copyright © 2023, The Authors. All rights reserved.

关键词： Transition metals

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Smart Insole: A Gait Analysis Monitoring Platform Targeting Parkinson Disease Patients Based on Insoles

arXiv

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arXiv 2022年

作者： Boucharas, Dimitrios G. Androutsos, Christos Gkois, George Tsakanikas, Vassilis D. Pezoulas, Vasileios C. Manousos, Dimitrios Skaramagkas, Vasileios Chatzaki, Chariklia Kontogiannis, Stathis Spandonidis, Christos Pantazis, Alexandros K. Tachos, Nikolaos S. Tsiknakis, Manolis Fotiadis, Dimitrios I. The Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina IoanninaGR-45110 Greece Crete HeraklionGR-70013 Greece Biomedical Informatics and eHealth Laboratory Department of Electrical and Computer Engineering Hellenic Mediterranean University Estavromenos Crete HeraklionGR-71004 Greece The Department of Electric and Computer Engineering Hellenic Mediterranean University Crete HeraklionGR-71004 Greece PD Neurotechnology Ltd IoanninaGR-45110 Greece Prisma Electronics SA P.Faliro AthensGR-17564 Greece Crete HeraklionGR-70013 Greece Biomedical Research Institute FORTH IoanninaGR-45110 Greece The Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR-45110 Greece

During the preceding decades, human gait analysis has been the center of attention for the scientific community, while the association between gait analysis and overall health monitoring has been extensively reported. Technological advances further assisted in this alignment, resulting in access to inexpensive and remote healthcare services. Various assessment tools, such as software platforms and mobile applications, have been proposed by the scientific community and the market that employ sensors to monitor human gait for various purposes ranging from biomechanics to the progression of functional recovery. The framework presented herein offers a valuable digital biomarker for diagnosing and monitoring Parkinson’s disease that can help clinical experts in the decision-making process leading to corrective planning or patient-specific treatment. More accurate and reliable decisions can be provided through a wide variety of integrated Artificial Intelligence algorithms and straightforward visualization techniques, including, but not limited to, heatmaps and bar plots. The framework consists of three core components: the insole pair, the mobile application, and the cloud-based platform. The insole pair deploys 16 plantar pressure sensors, an accelerometer, and a gyroscope to acquire gait data. The mobile application formulates the data for the cloud platform, which orchestrates the component interaction through the web application. Utilizing open communication protocols enables the straightforward replacement of one of the core components with a relative one (e.g., a different model of insoles), transparently from the end user, without affecting the overall architecture, resulting in a framework with the flexibility to adjust its modularity. © 2022, CC BY.

关键词： computer architecture

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