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Brain computed tomography reading of stroke patients by resident doctors from different medical specialities: An eye-tracking study

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Journal of Clinical Neuroscience 2023年 117卷 173-180页

作者： Liu, Chi-Hung Chang, Chun-Wei Hung, June Lin, John J.H. Sung, Pi-Shan Lee, Li-Ang Hsiao, Cheng-Ting Chao, Yi-Ping Huang, Elaine Shinwei Wang, Shu-Ling Department of Neurology Chang Gung Memorial Hospital Linkou Medical Center Taoyuan Taiwan School of Medicine College of Medicine Chang Gung University Taoyuan Taiwan Division of Medical Education Graduate Institute of Clinical Medical Sciences College of Medicine Chang Gung University Taoyuan Taiwan Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan Graduate Institute of Science Education National Taiwan Normal University Taipei City Taiwan Department of Neurology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Otorhinolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou Main Branch Taoyuan Taiwan Institute of Brain Science National Yang Ming Chiao Tung University Taipei Taiwan Department of Emergency Medicine Chang Gung Memorial Hospital Chiayi Taiwan Chang Gung Medical Education Research Centre Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Biomedical Engineering Chang Gung University Taoyuan Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taiwan

Background: Using the eye-tracking technique, our work aimed to examine whether difference in clinical background may affect the training outcome of resident doctors’ interpretation skills and reading behaviour related to brain computed tomography (CT). Methods: Twelve resident doctors in the neurology, radiology, and emergency departments were recruited. Each participant had to read CT images of the brain for two cases. We evaluated each participant's accuracy of lesion identification. We also used the eye-tracking technique to assess reading behaviour. We recorded dwell times, fixation counts, run counts, and first-run dwell times of target lesions to evaluate visual attention. Transition entropy was applied to assess the temporal relations and spatial dynamics of systematic image reading. Results: The eye-tracking results showed that the image reading sequence examined by transition entropy was comparable among resident doctors from different medical specialties (p = 0.82). However, the dwell time of the target lesions was shorter for the resident doctors from the neurology department (4828.63 ms, p = 0.01) than for those from the resident doctors from the radiology (6275.88 ms) and emergency (5305.00 ms) departments. The eye-tracking results in individual areas of interest only showed differences in the eye-tracking performance of the first-run dwell time (p = 0.05) in the anterior cerebral falx. Discussion: Our findings demonstrate that resident doctors from different medical specialties may achieve similar imaging reading patterns for brain CT. This may mitigate queries regarding the influence of different backgrounds on training outcomes. © 2023 Elsevier Ltd

关键词： Emergency department Eye-tracking Image reading behaviour Neurology Radiology

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A Multi-Channel Ratio-of-Ratios Method for Noncontact Hand Video Based SpO2 Monitoring Using Smartphone Cameras

arXiv

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arXiv 2021年

作者： Tian, Xin Wong, Chau-Wai Ranadive, Sushant M. Wu, Min Department of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Department of Electrical and Computer Engineering Forensic Sciences Cluster Secure Computing Institute NC State University RaleighNC27695 United States Department of Kinesiology School of Public Health University of Maryland College ParkMD20742 United States

Blood oxygen saturation (SpO2) is an important indicator for pulmonary and respiratory functionalities. Clinical findings on COVID-19 show that many patients had dangerously low blood oxygen levels not long before conditions worsened. It is therefore recommended, especially for the vulnerable population, to regularly monitor the blood oxygen level for precaution. Recent works have investigated how ubiquitous smartphone cameras can be used to infer SpO2. Most of these works are contact-based, requiring users to cover a phone’s camera and its nearby light source with a finger to capture reemitted light from the illuminated tissue. Contact-based methods may lead to skin irritation and sanitary concerns, especially during a pandemic. In this paper, we propose a noncontact method for SpO2 monitoring using hand videos acquired by smartphones. Considering the optical broadband nature of the red (R), green (G), and blue (B) color channels of the smartphone cameras, we exploit all three channels of RGB sensing to distill the SpO2 information beyond the traditional ratio-of-ratios (RoR) method that uses only two wavelengths. To further facilitate an accurate SpO2 prediction, we design adaptive narrow bandpass filters based on accurately estimated heart rate to obtain the most cardiac-related AC component for each color channel. Experimental results show that our proposed blood oxygen estimation method can reach a mean absolute error of 1.26% when a pulse oximeter is used as a reference, outperforming the traditional RoR method by 25%. Copyright © 2021, The Authors. All rights reserved.

关键词： Hemoglobin oxygen saturation

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Cost and impact of weak medium term policies in the electricity system in Western North America

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Electricity Journal 2021年第3期34卷 106925-106925页

作者： Hidalgo-Gonzalez, Patricia L. Johnston, Josiah Kammen, Daniel M. Energy and Resources Group University of California BerkeleyCA United States Electrical Engineering and Computer Sciences University of California BerkeleyCA United States Mechanical and Aerospace Engineering University of California San DiegoCA United States The Goldman School of Public Policy University of California BerkeleyCA United States

We study the cost and lock in of carbon intensive technologies due to weak medium-term policies. We use SWITCH WECC—a power system capacity expansion optimization model with high temporal and geographical resolution. We test three carbon cap scenarios. For each scenario, we optimize the power system for a medium timeframe (2030) and a long timeframe (2050). In the medium timeframe optimizations, by 2030 coal replaces gas power. This occurs because the long optimization foresees the stronger carbon cap in 2050. Therefore, it is optimal to transition towards cleaner technologies as early as 2030. The medium-term optimization has higher costs in 2040 and 2050 compared to the long optimization. Therefore, to minimize total costs to reduce emissions by 80 % in 2050, we should optimize until 2050 or have stronger carbon cap policies by 2030 (such as 26 % carbon emissions reductions from 1990 levels by 2030 across the WECC). © 2021 Elsevier Inc.

关键词： Carbon

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LEARNING TRANSFER OPERATORS BY KERNEL DENSITY ESTIMATION

arXiv

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arXiv 2022年

作者： Surasinghe, Sudam Fish, Jeremie Bollt, Erik M. Department of Ecology and Evolutionary Biology Yale University New HavenCT06520 United States Public Health Modeling Unit Yale School of Public Health New HavenCT06510 United States Clarkson Center for Complex Systems Science Department of Electrical and Computer Engineering Clarkson University 8 Clarkson Ave PotsdamNY13699 United States

Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method, involves projecting onto basis functions represented as characteristic functions supported over a fine grid of rectangles. From this perspective, the Ulam-Galerkin approach can be interpreted as density estimation using the histogram method. In this study, we recast the problem within the framework of statistical density estimation. This alternative perspective allows for an explicit and rigorous analysis of bias and variance, thereby facilitating a discussion on the mean square error. Through comprehensive examples utilizing the logistic map and a Markov map, we demonstrate the validity and effectiveness of this approach in estimating the eigenvectors of the Frobenius-Perron operator. We compare the performance of Histogram Density Estimation (HDE) and Kernel Density Estimation (KDE) methods and find that KDE generally outperforms HDE in terms of accuracy. However, it is important to note that KDE exhibits limitations around boundary points and jumps. Based on our research findings, we suggest the possibility of incorporating other density estimation methods into this field and propose future investigations into the application of KDE-based estimation for high-dimensional maps. These findings provide valuable insights for researchers and practitioners working on estimating the Frobenius-Perron operator and highlight the potential of density estimation techniques in this area of study. © 2022, CC BY.

关键词： Galerkin methods

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Whose tweets are surveilled for the police: An audit of a social-media monitoring tool via log files

arXiv

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arXiv 2020年

作者： Borradaile, Glencora Burkhardt, Brett LeClerc, Alexandria School of Electrical Engineering and Computer Science Oregon State University School of Public Policy Oregon State University

Social media monitoring by law enforcement is becoming commonplace, but little is known about what software packages for it do. Through public records requests, we obtained log files from the Corvallis (Oregon) Police department's use of social media monitoring software called DigitalStakeout. These log files include the results of proprietary searches by DigitalStakeout that were running over a period of 13 months and include 7240 social media posts. In this paper, we focus on the Tweets logged in this data and consider the racial and ethnic identity (through manual coding) of the users that are therein flagged by DigitalStakeout. We observe differences in the demographics of the users whose Tweets are flagged by DigitalStakeout compared to the demographics of the Twitter users in the region, however, our sample size is too small to determine significance. Further, the demographics of the Twitter users in the region do not seem to reflect that of the residents of the region, with an apparent higher representation of Black and Hispanic people. We also reconstruct the keywords related to a Narcotics report set up by DigitalStakeout for the Corvallis Police department and find that these keywords flag Tweets unrelated to narcotics or flag Tweets related to marijuana, a drug that is legal for recreational use in Oregon. Almost all of the keywords have a common meaning unrelated to narcotics (e.g. broken, snow, hop, high) that call into question the utility that such a keyword based search could have to law enforcement. As social media monitoring is increasingly used for law enforcement purposes, racial biases in surveillance may contribute to existing racial disparities in law enforcement practices. We are hopeful that log files obtainable through public records request will shed light on the operation of these surveillance tools. There are challenges in auditing these tools: public records requests may go unfulfilled even if the data is available, social media platfor

关键词： Law enforcement

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Wi-Fi Router Signal Coverage Position Prediction System using Machine Learning Algorithms

Wi-Fi Router Signal Coverage Position Prediction System usin...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： Rajalaxmi Hegde Sandeep Kumar Hegde Kdv Prasad Ved Srinivas Tanmoy De V Dankan Gowda Department of Computer Science and Engineering NITTE(Deemed to be University) NMAM Institute of Technology Nitte Karnataka India Symbiosis Institute of Business Management (SIBM) Symbiosis International (Deemed University) (SIU) Off Bangalore Highway Kothur Mandal Village Mahabubnagar Hyderabad Telangana India Public Policy Thiagarajar School of Management Madurai Tamil Nadu India Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India

As a popular topic, indoor positioning has gradually drawn the attention of both academia and business. Numerous location based services including healthcare, repository tracking, and security call for accurate estimation. An accurate estimation might be achieved by using additional location-sensing equipment, but this is not generally done because it would result in expensive brand specialization. A flexible and affordable location determination technique that exploits the already-existing WLAN infrastructure in indoor spaces has been designed without incurring additional costs, among all suggestions in the literature that include hardware and highly complex *** positioning strategy is becoming more popular. Soon in actual surroundings, WLAN will be able to be employed as part of an indoor positioning system. In comparison to similar systems, it is a good option in terms of accuracy, precision, and cost. It has also become the most user-friendly way, particularly with the widespread use of smartphones and tablet computers. In the literature, many machine learning algorithms such as cluster-filtered KNN and fuzzy c means algorithms have been proposed for the WiFi router signal coverage position prediction system but the disadvantage of these approaches is that it will consume much pre-processing time for reference points data and also these approaches found to be less accurate. In the proposed study, adaptive KNN-based machine learning model has been proposed for the WiFi signal coverage prediction system. The proposed method adjusts the value of K for each position by examining the relationship between the K value and the intensity of the received WiFi signal. This technique improves positioning accuracy by more than 30% when compared to the existing approach. The experimental results are conducted by applying various machine learning algorithms. The experimental finding demonstrates that the proposed approach obtained better results compared to tradi

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The IDEAL framework for surgical robotics: development, comparative evaluation and long-term monitoring (vol 30, pg 61, 2024)

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NATURE MEDICINE 2024年第4期30卷 1213-1213页

作者： Marcus, Hani J. Ramirez, Pedro T. Khan, Danyal Z. Horsfall, Hugo Layard Hanrahan, John G. Williams, Simon C. Beard, David J. Bhat, Rani Catchpole, Ken Cook, Andrew Hutchison, Katrina Martin, Janet Melvin, Tom Stoyanov, Danail Rovers, Maroeska Raison, Nicholas Dasgupta, Prokar Noonan, David Stocken, Deborah Sturt, Georgia Vanhoestenberghe, Anne Vasey, Baptiste McCulloch, Peter Department of Neurosurgery National Hospital of Neurology and Neurosurgery London UK Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Centre for Interventional and Surgical Sciences (WEISS) London UK Department of Obstetrics and Gynaecology Houston Methodist Hospital Neal Cancer Center Houston TX USA RCS Surgical Interventional Trials Unit (SITU) & Robotic and Digital Surgery Initiative (RADAR) Nuffield Dept Orthopaedics Rheumatology and Musculo-skeletal Sciences University of Oxford Oxford UK Department of Gynaecological Oncology Apollo Hospital Bengaluru India Department of Anaesthesia and Perioperative Medicine Medical University of South Carolina Charleston SC USA NIHR Coordinating Centre and Clinical Trials Unit University of Southampton Southampton UK Department of Philosophy Macquarie University Sydney Australia Department of Anesthesia & Perioperative Medicine University of Western Ontario Ontario Canada Department of Medical Gerontology School of Medicine Trinity College Dublin Dublin Republic of Ireland Department of Medical Imaging Radboudumc Nijmegen the Netherlands Department of Biomedical Engineering and Imaging Sciences King’s College London London UK King’s Health Partners Academic Surgery King’s College London London UK Moon Surgical Paris France RCSEng Surgical Trials Centre Leeds Institute of Clinical Trials Research University of Leeds Leeds UK Bowel Research UK London UK School of Biomedical Engineering & Imaging Sciences King’s College London London UK Department of Surgery Geneva University Hospital Geneva Switzerland Nuffield Department of Surgical Sciences University of Oxford John Radcliffe Hospital Oxford UK PRISMA Lab University of Naples Federico II Naples Italy Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich Switzerland School of Computer Science University of Birmingham Birmingham UK Division of Cardiac Surgery Brigham and Women’s Hospital Harvard Medical Schoo

The next generation of surgical robotics is poised to disrupt healthcare systems worldwide, requiring new frameworks for evaluation. However, evaluation during a surgical robot’s development is challenging due to their complex evolving nature, potential for wider system disruption and integration with complementary technologies like artificial intelligence. Comparative clinical studies require attention to intervention context, learning curves and standardized outcomes. Long-term monitoring needs to transition toward collaborative, transparent and inclusive consortiums for real-world data collection. Here, the Idea, Development, Exploration, Assessment and Long-term monitoring (IDEAL) Robotics Colloquium proposes recommendations for evaluation during development, comparative study and clinical monitoring of surgical robots—providing practical recommendations for developers, clinicians, patients and healthcare systems. Multiple perspectives are considered, including economics, surgical training, human factors, ethics, patient perspectives and sustainability. Further work is needed on standardized metrics, health economic assessment models and global applicability of recommendations.

关键词： Medical research Surgery

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Cardiac strain imaging with dynamically skipped frames: A simulation study

Cardiac strain imaging with dynamically skipped frames: A si...

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2020 IEEE International Ultrasonics Symposium, IUS 2020

作者： Mukaddim, Rashid Al Varghese, Tomy University of Wisconsin - Madison Department of Electrical and Computer Engineering MadisonWI53706 United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin - Madison MadisonWI-53706 United States

ISBN: (纸本)9781728154480

Accurate lateral displacement estimation is a critical requirement to reliably use cardiac strain imaging for myocardial health or carotid strain imaging to assess atherosclerosis. Estimation of small lateral displacements are challenging even with 2-D kernels. We investigate the feasibility of using a dynamic frame skip (DFS) approach to improve lateral strain estimation for cardiac and carotid strain imaging. DFS automatically determines the optimal frame skip using axial strain information estimated during displacement tracking. We incorporated DFS into a hierarchical Bayesian regularization based displacement tracking method for cardiac and carotid strain imaging. A finite element analysis (FEA) model of canine cardiac deformation was utilized to quantify estimation performance with DFS and compare against conventional inter-frame displacement (CFS) approach. DFS displacement and strain temporal curves showed improved qualitative agreement with FEA ground truth compared to CFS results. Consequently, DFS had lower strain total temporal relative (TTR) strain errors (TTRaxial= 18.37 %, TTRlateral= 29.91 %, TTRradial = 17.32 %, and TTRlongitudinal = 14.51 %) compared to CFS (TTRaxial= 22.39 %, TTRlateral = 30.88 %, TTRradial = 22.35 %, and TTRlongitudinal= 15.87 %). These results show that DFS can potentially improve cardiac strain estimation quality. © 2020 IEEE.

关键词： Heart

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Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study

arXiv

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arXiv 2023年

作者： Kurbatskaya, Anna Jaramillo-Jimenez, Alberto Ochoa-Gomez, John Fredy Brønnick, Kolbjørn Fernandez-Quilez, Alvaro The Department of Electrical Engineering and Computer Science Faculty of Science and Technology University of Stavanger Stavanger Norway Department of Psychiatry Stavanger University Hospital The Department of Public Health Faculty of Health Sciences University of Stavanger Stavanger Norway Grupo de Neurociencias de Antioquia Universidad de Antioquia School of Medicine Medellín Colombia Grupo Neuropsicología y Conducta Universidad de Antioquia School of Medicine Medellín Colombia

Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection before evaluating the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively. Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data. © 20

关键词： Pipelines

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CT-based characterization of transverse and longitudinal trabeculae and its applications

CT-based characterization of transverse and longitudinal tra...

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Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Zhang, Xiaoliu Letuchy, Elena M. Levy, Steven M. Torner, James C. Saha, Punam K. Department of Electrical and Computer Engineering University of Iowa United States College of Public Health University of Iowa United States Department of Preventive and Community Dentistry University of Iowa United States Department of Epidemiology University of Iowa United States

ISBN: (纸本)9781510634015

Osteoporosis is a common age-related disease characterized by reduced bone mineral density (BMD), micro-structural deterioration, and enhanced fracture-risk. Although, BMD is clinically used to define osteoporosis, there are compelling evidences that bone micro-structural properties are strong determinants of bone strength and fracture-risk. Reliable measures of effective trabecular bone (Tb) micro-structural features are of paramount clinical significance. Tb consists of transverse and longitudinal micro-structures, and there is a hypothesis that transverse trabeculae improve bone strength by arresting buckling of longitudinal trabeculae. In this paper, we present an emerging clinical CT-based new method for characterizing transverse and longitudinal trabeculae, validate the method, and examine its application in human studies. Specifically, we examine repeat CT scan reproducibility, and evaluate the relationships of these measures with gender and body size using human CT data from the Iowa Bone Development Study (IBDS) (n = 99;49 female). Based on a cadaveric ankle study (n = 12), both transverse and longitudinal Tb measures are found reproducible (ICC > 0.94). It was observed in the IBDS human data that males have significantly higher trabecular bone measures than females for both inner (p © 2020 SPIE

关键词： computerized tomography

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