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Informatics in Medicine Unlocked 2020年 20卷

作者： Sharifmousavi, Seyed Sajjad Borhani, Matia Sadat Biomedical Engineering Department Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Biology Department Faculty of Sciences Gonbad Kavous University Golestan Iran

Background: It has been reported that some nutritional factors can play a significant role in immune system regulation and the etiology of immune-mediated diseases such as multiple sclerosis (MS). However, there have been no studies on the relationship between nutritional factors and MS as a primary diagnostic method. Methods: Plasma samples of 99 MS patients and 81 healthy people as the control group were assayed using atomic absorption spectroscopy and the chemical autoanalyzer method to determine the amount of selenium and vitamins (B₁₂, D₃), respectively. To find a diagnostic model, three types of supervised machine learning methods, i.e., support vector machine algorithm (SVM), decision tree (DT), and K-nearest neighbor (KNN) were used. Results: The diagnostic model based on the support vector machine method had high accuracy (98.89%), sensitivity (98.98%), and positive predictive value (98.98%). Moreover, the SVM had a true positive rate of 99.9%. Conclusion: The results of this study showed that the SVM algorithm with Radial Basis Function Kernel could be effective in obtaining a good MS diagnostic method based on the plasma levels of selenium, vitamin B₁₂, and vitamin D₃ regarding DT and KNN. This method is non-invasive and cost-effective and can be used as a primary screening method for the diagnosis of MS. © 2020

关键词： Multiple sclerosis Selenium Support vector machine algorithm Vitamin B12 Vitamin D3

A wireless forehead e-tattoo for mental workload estimation

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Device

Device 2025年

作者： Huh, Heeyong Shin, Hyonyoung Li, Hongbian Hirota, Kazuma Hoang, Carolyn Thangavel, Shrikar Feltman, Kathryn A. Sentis, Luis Lu, Nanshu Department of Aerospace Engineering and Engineering Mechanics The University of Texas at Austin Austin 78712 TX United States Department of Electrical and Computer Engineering The University of Texas at Austin Austin 78712 TX United States Department of Mechanical Engineering The University of Texas at Austin Austin 78712 TX United States U.S. Army Aeromedical Research Laboratory Fort Novosel 36362 AL United States Department of Biomedical Engineering The University of Texas at Austin Austin 78712 TX United States Texas Materials Institute The University of Texas at Austin Austin 78712 TX United States

Real-time monitoring of operator cognitive states can enhance the safety and performance of human-in-the-loop systems. However, traditional electroencephalography (EEG) and electrooculography (EOG) devices, with their bulkiness, dangling wires, and time-consuming setups, are restricting or impossible for monitoring operators’ cognitive states while performing tasks in reality. In this study, we propose a wireless forehead EEG and EOG sensor designed to be as thin and conformable to the skin as a temporary tattoo sticker, which is referred to as a forehead e-tattoo. This e-tattoo employs adhesive poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) composite-coated graphite-deposited polyurethane (APC-GPU) electrodes laminated with a battery-powered flexible printed circuit (FPC) for data acquisition and wireless transmission. The forehead EEG and EOG measured by this e-tattoo are correlated with mental workload during a dual N-back working memory task. The results confirm the feasibility of applying the wireless, low-profile, forehead e-tattoo for mental workload estimation. © 2025 Elsevier Inc.

关键词： cognitive load DTI-4: Validate e-tattoo EEG EOG forehead EEG mental workload N-back wearable EEG

Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time 21

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Deep inference of latent dynamics with spatio-temporal super...

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Feng Zhu Andrew R. Sedler Harrison A. Grier Nauman Ahad Mark A. Davenport Matthew T. Kaufman Andrea Giovannucci Chethan Pandarinath Neuroscience Graduate Program Emory University Center for Machine Learning Georgia Tech Computational Neuroscience Graduate Program The University of Chicago School of Electrical and Computer Engineering Georgia Tech Dept. of Organismal Biology and Anatomy The University of Chicago and Neuroscience Institute The University of Chicago Joint Dept. of Biomedical Engineering UNC Chapel Hill and NC State University and UNC Chapel Hill Neuroscience Center and NC State/UNC Closed-Loop Engineering for Advanced Rehabilitation (CLEAR) Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech and Dept. of Neurosurgery Emory University

ISBN: (纸本)9781713845393

Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial sampling (more channels or pixels) and the temporal frequency of sampling. Here we demonstrate that it is possible to obtain spatio-temporal super-resolution in neuronal time series by exploiting relationships among neurons, embedded in latent low-dimensional population dynamics. Our novel neural network training strategy, selective backpropagation through time (SBTT), enables learning of deep generative models of latent dynamics from data in which the set of observed variables changes at each time step. The resulting models are able to infer activity for missing samples by combining observations with learned latent dynamics. We test SBTT applied to sequential autoencoders and demonstrate more efficient and higher-fidelity characterization of neural population dynamics in electrophysiological and calcium imaging data. In electrophysiology, SBTT enables accurate inference of neuronal population dynamics with lower interface bandwidths, providing an avenue to significant power savings for implanted neuroelectronic interfaces. In applications to two-photon calcium imaging, SBTT accurately uncovers high-frequency temporal structure underlying neural population activity, substantially outperforming the current state-of-the-art. Finally, we demonstrate that performance could be further improved by using limited, high-bandwidth sampling to pretrain dynamics models, and then using SBTT to adapt these models for sparsely-sampled data.

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AI-driven 3D Spatial Transcriptomics

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

Using anisotropic 3D minkowski functionals for trabecular bone characterization and biomechanical strength prediction in proximal femur specimens

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arXiv 2020年

作者： Nagarajan, Mahesh B. De, Titas Lochmüller, Eva-Maria Eckstein, Felix Wismüller, Axel Departments of Biomedical Engineering & Imaging Sciences University of Rochester United States Department of Electrical & Computer Engineering University of Rochester United States Institute of Anatomy Paracelsus Medical University Salzburg Salzburg Austria

The ability of Anisotropic Minkowski Functionals (AMFs) to capture local anisotropy while evaluating topological properties of the underlying gray-level structures has been previously demonstrated. We evaluate the ability of this approach to characterize local structure properties of trabecular bone micro-architecture in ex vivo proximal femur specimens, as visualized on multi-detector CT, for purposes of biomechanical bone strength prediction. To this end, volumetric AMFs were computed locally for each voxel of volumes of interest (VOI) extracted from the femoral head of 146 specimens. The local anisotropy captured by such AMFs was quantified using a fractional anisotropy measure;the magnitude and direction of anisotropy at every pixel was stored in histograms that served as a feature vectors that characterized the VOIs. A linear multi-regression analysis algorithm was used to predict the failure load (FL) from the feature sets;the predicted FL was compared to the true FL determined through biomechanical testing. The prediction performance was measured by the root mean square error (RMSE) for each feature set. The best prediction performance was obtained from the fractional anisotropy histogram of AMF Euler Characteristic (RMSE = 1.01 ± 0.13), which was significantly better than MDCT-derived mean BMD (RMSE = 1.12 ± 0.16, p Copyright © 2020, The Authors. All rights reserved.

关键词： Risk assessment

Temporal Uncertainty Localization to Enable Human-in-the-loop Analysis of Dynamic Contrast-enhanced Cardiac MRI Datasets

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arXiv 2023年

作者： Yalcinkaya, Dilek M. Youssef, Khalid Heydari, Bobak Simonetti, Orlando Dharmakumar, Rohan Raman, Subha Sharif, Behzad IndianapolisIN United States Elmore Family School of Electrical & Computer Engineering Purdue University West LafayetteIN United States Krannert Cardiovascular Research Center IUSM IU Health Cardiovascular Institute IndianapolisIN United States Stephenson Cardiac Imaging Centre University of Calgary Alberta Canada Department of Internal Medicine Division of Cardiovascular Medicine Davis Heart and Lung Research Institute The Ohio State University ColumbusOH United States Weldon School of Biomedical Eng. Purdue University West LafayetteIN United States

Dynamic contrast-enhanced (DCE) cardiac magnetic resonance imaging (CMRI) is a widely used modality for diagnosing myocardial blood flow (perfusion) abnormalities. During a typical free-breathing DCE-CMRI scan, close to 300 time-resolved images of myocardial perfusion are acquired at various contrast "wash in/out" phases. Manual segmentation of myocardial contours in each time-frame of a DCE image series can be tedious and time-consuming, particularly when non-rigid motion correction has failed or is unavailable. While deep neural networks (DNNs) have shown promise for analyzing DCE-CMRI datasets, a "dynamic quality control" (dQC) technique for reliably detecting failed segmentations is lacking. Here we propose a new space-time uncertainty metric as a dQC tool for DNN-based segmentation of free-breathing DCE-CMRI datasets by validating the proposed metric on an external dataset and establishing a human-in-the-loop framework to improve the segmentation results. In the proposed approach, we referred the top 10% most uncertain segmentations as detected by our dQC tool to the human expert for refinement. This approach resulted in a significant increase in the Dice score (p © 2023, CC BY.

关键词： Deep neural networks

A cascaded residual UNET for fully automated segmentation of prostate and peripheral zone in T2-weighted 3D fast spin echo images

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arXiv 2020年

作者： Umapathy, Lavanya Unger, Wyatt Shareef, Faryal Arif, Hina Martin, Diego Altbach, Maria Bilgin, Ali Electrical and Computer Engineering University of Arizona TucsonAZ United States Medical Imaging University of Arizona TucsonAZ United States Biomedical Engineering University of Arizona TucsonAZ United States

Multi-parametric MR images have been shown to be effective in the non-invasive diagnosis of prostate cancer. Automated segmentation of the prostate eliminates the need for manual annotation by a radiologist which is time consuming. This improves efficiency in the extraction of imaging features for the characterization of prostate tissues. In this work, we propose a fully automated cascaded deep learning architecture with residual blocks, Cascaded MRes-UNET, for segmentation of the prostate gland and the peripheral zone in one pass through the network. The network yields high Dice scores (0.91±.02), precision (0.91±.04), and recall scores (0.92±.03) in prostate segmentation compared to manual annotations by an experienced radiologist. The average difference in total prostate volume estimation is less than 5%. © 2020, CC BY-NC-ND.

关键词： Urology

Nonequilibrium transport and the fluctuation theorem in the thermodynamic behaviors of nonlinear photonic systems

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arXiv 2024年

作者： Liu, Yang Lu, Jincheng Xiong, Zhongfei Wu, Fan O. Christodoulides, Demetrios Chen, Yuntian Jiang, Jian-Hua School of Physical Science and Technology Collaborative Innovation Center of Suzhou Nano Science and Technology Soochow University 1 Shizi Street Suzhou215006 China Jiangsu Key Laboratory of Micro and Nano Heat Fluid Flow Technology and Energy Application School of Physical Science and Technology Suzhou University of Science and Technology Suzhou215009 China School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan430074 China CREOL College of Optics and Photonics University of Central Florida OrlandoFL32816-2700 United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA90089 United States School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Physics University of Science and Technology of China Hefei230026 China

Nonlinear multimode optical systems have attracted substantial attention due to their rich physical properties. Complex interplay between the nonlinear effects and mode couplings makes it difficult to understand the collective dynamics of photons. Recent studies show that such collective phenomena can be effectively described by a Rayleigh–Jeans thermodynamics theory which is a powerful tool for the study of nonlinear multimode photonic systems. These systems, in turn, offer a compelling platform for investigating fundamental issues in statistical physics, attributed to their tunability and the ability to access negative temperature regimes. However, to date, a theory for the nonequilibrium transport and fluctuations is yet to be established. Here, we employ the full counting statistics theory to study the nonequilibrium transport of particle and energy in nonlinear multimode photonic systems in both positive and negative temperature regimes. Furthermore, we discover that in situations involving two reservoirs of opposite temperatures and chemical potentials, an intriguing phenomenon known as the loop current effect can arise, wherein the current in the positive energy sector runs counter to that in the negative energy sector. In addition, we numerically confirm that the fluctuation theorem remains applicable in optical thermodynamics systems across all regimes, from positive temperature to negative ones. Our findings closely align with numerical simulations based on first-principles nonlinear wave equations. Our work seeks to deepen the understanding of irreversible non-equilibrium processes and statistical fluctuations in nonlinear many-body photonic systems which will enhance our grasp of collective phenomena of photons and foster a fruitful intersection between optics and statistical physics. © 2024, CC BY-NC-ND.

关键词： Temperature

Corrigendum to “Non- invasive Localization of the Ectopic Foci of Focal Atrial Tachycardia by Using ECG Signal based Sparse Decomposition Algorithm”

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biomedical Signal Processing and Control 2022年 71卷

作者： Fatemeh Mohammadi Ali Sheikhani Farbod Razzazi Alireza Ghorbani Sharif Biomedical Engineering Faculty of Medical Sciences and Technologies Science and Research Branch Islamic Azad University Tehran Iran Electrical Engineering Faculty of Electrical and Computer Engineering Science Science and Research Branch Islamic Azad University Tehran Iran Cardiologist Interventional Electrophysiologist of The Arrhythmia Centre Tehran Iran

A Mathematical Model of Lung Functionality using Pressure Signal for Volume-Controlled Ventilation

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A Mathematical Model of Lung Functionality using Pressure Si...

IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Husam Y. Al-Hetari Muhammad Nomani Kabir Mahmoud A. Al-Rumaima Noman Q. Al-Naggar Yasser M. Alginahi Md Munirul Hasan Faculty of Computing University Malaysia Pahang Malaysia Kuantan Malaysia Dept. of Biomedical Engineering University of Science and Technology Yemen Dept. of Electrical and Computer Engineering University of Windsor Windsor Canada

ISBN: (数字)9781728161334

ISBN: (纸本)9781728161341

Mechanical Ventilation is used to support the respiratory system malfunction by assisting recovery breathing process which could result from diseases and viruses such as pneumonia and COVID-19. Mathematical models are used to study and simulate the respiratory system supported by mechanical ventilation using different modes such as volume-controlled ventilation (VCV). In this research, a single compartment lung model ventilated by VCV is developed during real time mechanical ventilation using pressure signal. This mathematical model describes the lung volume and compliance correctly considering positive end expiration pressure (PEEP) value. The model is implemented using LabVIEW tools and can be used to monitor the volume, flow and compliance as outputs of the model. Two experiments are carried out on the proposed lung model at three input scenarios of volume (400, 500 and 600 ml) for each experiment considering a PEEP value. To validate the model, an artificial lung connected to a VCV with the same scenarios is used. Validation check is conducted by comparing the outputs of the lung model to that of the artificial lung. The experimental results showed that the measured lung model outputs with negative feedback are the same for pressure and flow as the outputs without negative feedback, whereas the measured volume is comparatively lower for negative feedback. Average percent error in the experiment with negative feedback (5.14%) is smaller compared to the experiment without negative feedback (9.28%). Furthermore, the average error of the calculated compliance decreases from 16% (without negative feedback) to 2% (with negative feedback). The obtained results of the proposed method showed good performance and acceptable accuracy. Thus, the model facilitates the clinicians and practitioners as a training tool to learn real-time mechanical ventilation functionalities.

关键词： Lung Ventilation Mathematical model Solid modeling Negative feedback Atmospheric modeling Real-time systems