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Journal of Polymer Research 2022年第9期29卷 1-1页

作者： Abdullah, Turdimuhammad Saeed, Usman Memic, Adnan Gauthaman, Kalamegam Hussain, Mohammad Asif Al-Turaif, Hamad Department of Chemical and Materials Engineering Faculty of Engineering King Abdul Aziz University Jeddah Saudi Arabia Center of Nanotechnology King Abdul Aziz University Jeddah Saudi Arabia Center of Excellence in Genomic Medicine Research King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering (Biomedical Engineering Option) King Abdulaziz University Jeddah Saudi Arabia

Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment

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Brain Informatics 2020年第1期7卷 19页

作者： Rangaprakash, D. Odemuyiwa, Toluwanimi Narayana Dutt, D. Deshpande, Gopikrishna Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School BostonMA United States Division of Health Sciences and Technology Harvard University and Massachusetts Institute of Technology CambridgeMA United States Division of Engineering Science Faculty of Applied Science & Engineering University of Toronto TorontoON Canada Department of Electrical Communication Engineering Indian Institute of Science Bangalore India AU MRI Research Center Department of Electrical and Computer Engineering Auburn University 560 Devall Dr Suite 266D AuburnAL36849 United States Department of Psychological Sciences Auburn University AuburnAL United States Alabama Advanced Imaging Consortium University of Alabama Birmingham Alabama United States Center for Health Ecology and Equity Research Auburn University AuburnAL United States Center for Neuroscience Auburn University AuburnAL United States School of Psychology Capital Normal University Beijing China Key Laboratory for Learning and Cognition Capital Normal University Beijing China Department of Psychiatry National Institute of Mental Health and Neurosciences Bangalore India

Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, DBSCAN and OPTICS, in accurately identifying individuals with three stages of cognitive impairment, including Alzheimer’s disease. We used static and dynamic functional connectivity features for clustering, which captures the strength and temporal variation of brain connectivity respectively. To assess the robustness of clustering to noise/outliers, we propose a novel method called recursive-clustering using additive-noise (R-CLAN). Results demonstrated that both clustering algorithms were effective, although OPTICS with dynamic connectivity features outperformed in terms of cluster purity (95.46%) and robustness to noise/outliers. This study demonstrates that density-based clustering can accurately and robustly identify diagnostic classes in an unsupervised way using brain connectivity. © 2020, The Author(s).

关键词： Magnetic resonance imaging

IMAGING DYNAMICS BENEATH TURBID MEDIA VIA PARALLELIZED SINGLE-PHOTON DETECTION

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arXiv 2021年

作者： Xu, Shiqi Yang, Xi Liu, Wenhui Jonsson, Joakim Qian, Ruobing Chandra Konda, Pavan Zhou, Kevin C. Kreiss, Lucas Dai, Qionghai Wang, Haoqian Berrocal, Edouard Horstmeyer, Roarke Department of Biomedical Engineering Duke University DurhamNC27708 United States Department of Automation Tsinghua University Beijing100084 China Division of Combustion Physics Department of Physics Lund University 22100 Sweden Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, we utilize a single-photon avalanche diode (SPAD) array camera to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. We demonstrate the ability to reconstruct images of transient (0.1-0.4s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution, and highlight how our model can flexibly extend to monitor flow speed within buried phantom vessels. © 2021, CC BY.

关键词： Dynamics

Spectroscopic detection of pigments in tissues: correlation with tissue aging and cancer development

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Spectroscopic detection of pigments in tissues: correlation ...

International Conference on Laser Optics

作者： L. M. Oliveira T. M. Gonç alves A. R. Botelho I. S. Martins H. F. Silva I. Carneiro S. Carvalho R. Henrique V. V. Tuchin ISEP Center of Innovation in Engineering and Industrial Technology Porto Portugal Physics Department Polytechnic of Porto - School of Engineering (ISEP) Porto Portugal Department of Electrical and Computer Engineering Porto University - School of Engineering Porto Portugal Department of Pathology and Cancer Biology and Epigenetics Group Portuguese Oncology Institute of Porto Porto Portugal Dept. of Pathological Cytological and Thanatological Anatomy Polytechnic of Porto - School of Health (ESS) Porto Portugal Deptartment of Pathology Santa Luzia Hospital (ULSAM) Viana do Castelo Portugal Dept. of Pat hol. and Molecular Immunology Porto University - Institute of Biomedical Sciences Abel Salazar Porto Portugal Science Medical Center Saratov State University Saratov Russia Laboratory of Laser Molecular Imaging and Machine Learning National Research Tomsk State University Tomsk Russia Laboratory of Laser Diagnostics of Technical and Living Systems Institute of Precision Mechanics and Control FRC &#x2018 Saratov Scientific Centre&#x2019 of the Russian Academy of Sciences Saratov Russia

ISBN: (数字)9781665466646

ISBN: (纸本)9781665466653

The direct calculation of the absorption coefficient spectra of various tissues from spectral measurements allowed to retrieve the contents of melanin and lipofuscin. In the rabbit brain cortex, 1.8 times higher melanin content is explained by the neuron degeneration process. Similar melanin and lipofuscin contents were found in the rabbit pancreas as a result of the tissue aging process. The conversion of 83 % of the melanin in the human normal kidney into lipofuscin in the cancer kidney indicates that lipofuscin can be considered a kidney cancer marker in humans.

关键词： Rabbits Absorption Neurons Lasers Aging Pigments Optics

Authors' Responses to Peer Reviews of “Supporting Technologies for COVID-19 Prevention: Systemized Review”

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JMIRx Med 2022年第2期3卷

作者： Zhuo Zhao Rui Li Yangmyung Ma Iman Islam Abdul M Azam Rajper WenZhan Song Hongliang Ren Zion Tsz Ho Tse School of Electrical and Computer Engineering University of Georgia Athens GA United States Tandon School of Engineering New York University Brooklyn NY United States Hull York Medical School University of York Heslington York United Kingdom Department of Computer Science University of Georgia Athens GA United States Department of Biomedical Engineering National University of Singapore Singapore Singapore Department of Electronic Engineering University of York York United Kingdom

Ensemble model for pre-discharge icd10 coding prediction

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arXiv 2020年

作者： Shaalan, Yassien Dokumentov, Alexander Khumrin, Piyapong Khwanngern, Krit Wisetborisut, Anawat Hatsadeang, Thanakom Karaket, Nattapat Achariyaviriya, Witthawin Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Siganakis, Terence Growing Data Yassein Alexander BioMedical Informatics Center Faculty of Medicine Chiang Mai University Department of Surgery Faculty of Medicine Chiang Mai University Department of Family Medicine Faculty of Medicine Chiang Mai University Department of Computer Engineering Faculty of Engineering Chiang Mai University Department of Electrical Engineering Faculty of Engineering Chiang Mai University Biomedical Engineering Institute Chiang Mai University Maharaj Nakhon Chiang Mai hospital Chiang Mai Thailand IT department of Maharaj Nakhon Chiang Mai hospital

The translation of medical diagnosis to clinical coding has wide range of applications in billing, aetiology analysis, and auditing. Currently, coding is a manual effort while the automation of such task is not straight forward. Among the challenges are the messy and noisy clinical records, case complexities, along with the huge ICD10 code space. Previous work mainly relied on discharge notes for prediction and was applied to a very limited data scale. We propose an ensemble model incorporating multiple clinical data sources for accurate code predictions. We further propose an assessment mechanism to provide confidence rates in predicted outcomes. Extensive experiments were performed on two new real-world clinical datasets (inpatient & outpatient) with unaltered case-mix distributions from Maharaj Nakorn Chiang Mai Hospital. We obtain multi-label classification accuracies of 0.73 and 0.58 for average precision, 0.56 and 0.35 for F1-scores and 0.71 and 0.4 accuracy in predicting principal diagnosis for inpatient and outpatient datasets respectively. © 2020, CC BY-NC-ND.

关键词： Forecasting

Toward autonomous robotic micro-suturing using optical coherence tomography calibration and path planning

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arXiv 2020年

作者： Tian, Yuan Draelos, Mark Tang, Gao Qian, Ruobing Kuo, Anthony Izatt, Joseph Hauser, Kris Department of Electrical and Computer Engineering Duke University DurhamNC United States Department of Biomedical Engineering Duke University DurhamNC United States Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States Department of Ophthalmology Duke University Medical Center DurhamNC United States

Robotic automation has the potential to assist human surgeons in performing suturing tasks in microsurgery, and in order to do so a robot must be able to guide a needle with sub-millimeter precision through soft tissue. This paper presents a robotic suturing system that uses 3D optical coherence tomography (OCT) system for imaging feedback. Calibration of the robot-OCT and robot-needle transforms, wound detection, keypoint identification, and path planning are all performed automatically. The calibration method handles pose uncertainty when the needle is grasped using a variant of iterative closest points. The path planner uses the identified wound shape to calculate needle entry and exit points to yield an evenly-matched wound shape after closure. Experiments on tissue phantoms and animal tissue demonstrate that the system can pass a suture needle through wounds with 0.200 mm overall accuracy in achieving the planned entry and exit points, and over 20× more precise than prior autonomous suturing robots. Copyright © 2020, The Authors. All rights reserved.

关键词： Needles

A closed-form localization method utilizing pseudorange measurements from two non-synchronized positioning systems

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arXiv 2020年

作者： Zhao, Sihao Zhang, Xiao-Ping Cui, Xiaowei Lu, Mingquan Department of Electrical Computer and Biomedical Engineering Ryerson University TorontoONM5B 2K3 Canada Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China

In a time-of-arrival (TOA) or pseudorange based positioning system, user location is obtained by observing multiple anchor nodes (AN) at known positions. Utilizing more than one positioning systems, e.g., combining Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS), brings better positioning accuracy. However, ANs from two systems are usually synchronized to two different clock sources. Different from single-system localization, an extra user-to-system clock offset needs to be handled. Existing dual-system methods either have high computational complexity or sub-optimal positioning accuracy. In this paper, we propose a new closed-form dual-system localization (CDL) approach that has low complexity and optimal localization accuracy. We first convert the nonlinear problem into a linear one by squaring the distance equations and employing intermediate variables. Then, a weighted least squares (WLS) method is used to optimize the positioning accuracy. We prove that the positioning error of the new method reaches Cramér-Rao Lower Bound (CRLB) in far field conditions with small measurement noise. Simulations on 2D and 3D positioning scenes are conducted. Results show that, compared with the iterative approach, which has high complexity and requires a good initialization, the new CDL method does not require initialization and has lower computational complexity with comparable positioning accuracy. Numerical results verify the theoretical analysis on positioning accuracy, and show that the new CDL method has superior performance over the state-of-the-art closed-form method. Experiments using real GPS and BDS data verify the applicability of the new CDL method and the superiority of its performance in the real world. © 2020, CC-BY-NC-ND.

关键词： Global positioning system

Privacy-preserving deep learning computation for geo-distributed medical big-data platforms

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arXiv 2020年

作者： Jeon, Joohyung Kim, Junhui Kim, Joongheon Kim, Kwangsoo Mohaisen, Aziz Kim, Jong-Kook School of Computer Science and Engineering Chung-Ang University Seoul Korea Republic of Biomedical Research Institute Center for Medical Innovation Seoul National University Hospital Seoul Korea Republic of Department of Computer Science University of Central Florida OrlandoFL United States School of Electrical Engineering Korea University Seoul Korea Republic of

This paper proposes a distributed deep learning framework for privacy-preserving medical data training. In order to avoid patients' data leakage in medical platforms, the hidden layers in the deep learning framework are separated and where the first layer is kept in platform and others layers are kept in a centralized server. Whereas keeping the original patients' data in local platforms maintain their privacy, utilizing the server for subsequent layers improves learning performance by using all data from each platform during training. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review

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Current treatment options in psychiatry 2025年第1期12卷 23页

作者： Jiyeong Kim Juan Pablo Gonzalez Pacheco Ashleigh Golden Elias Aboujaoude Peter van Roessel Aayushi Gandhi Pavithra Mukunda Tatevik Avanesyan Haopeng Xue Ehsan Adeli Jane Paik Kim Manish Saggar Shannon Wiltsey Stirman Eric Kuhn Kaustubh Supekar Kilian M Pohl Carolyn I Rodriguez Stanford Center for Digital Health Stanford School of Medicine Stanford CA USA. Department of Psychiatry and Behavioral Sciences Stanford School of Medicine Stanford CA USA. Program in Internet Health and Society Cedars-Sinai Medical Center Los Angeles CA USA. Artificial Intelligence for Mental Health Initiative (AI4MH) Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Department of Biomedical Data Science Stanford School of Medicine Stanford CA USA. Dissemination and Training Division National Center for PTSD VA Palo Alto Health Care System Palo Alto CA USA. Veterans Affairs Palo Alto Health Care System Palo Alto CA USA. Department of Electrical Engineering Stanford University Stanford CA USA.

Purpose of Review:Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings:While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary:AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care. Supplementary Information:The online version contains supplementary material available at 10.1007/s40501-025-00359-8.

关键词： AI Diagnosis Mental health OCD Obsessive-compulsive disorder Treatment