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JACC: Advances 2024年第10期3卷 101249-101249页

作者： Han, Donghee Shanbhag, Aakash Miller, Robert JH. Kwok, Nicholas Waechter, Parker Builoff, Valerie Newby, David E. Dey, Damini Berman, Daniel S. Slomka, Piotr Departments of Medicine (Division of Artificial Intelligence in Medicine) Imaging and Biomedical Sciences Cedars-Sinai Medical Center Los Angeles CA United States Signal and Image Processing Institute Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Cardiac Sciences University of Calgary Calgary AB Canada British Heart Foundation Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom

Background: Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives: The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods: We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results: A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (−9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions: The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR. © 2024 The Authors

关键词： artificial intelligence cardiac magnetic resonance imaging computed tomography myocardial mass

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Developing a Novel Citizen-Scientist Smartphone App for Collecting Behavioral and Affective Data from Children Populations 8th

Developing a Novel Citizen-Scientist Smartphone App for Coll...

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8th EAI International Conference on Mobile Communication and Healthcare, MobiHealth 2019

作者： Maramis, Christos Ioakimidis, Ioannis Kilintzis, Vassilis Stefanopoulos, Leandros Lekka, Eirini Papapanagiotou, Vasileios Diou, Christos Delopoulos, Anastasios Kassari, Penio Charmandari, Evangelia Maglaveras, Nikolaos Department of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Department of Biosciences and Nutrition Karolinska Institutet Huddinge Sweden Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Biomedical Research Foundation Academy of Athens Athens Greece Department of Industrial Engineering and Management Sciences Northwestern University Evanston United States

ISBN: (纸本)9783030492885

The paradigm of citizen-science, i.e., scientific research that is conducted in whole or in part by non-professional scientists, has gained popularity lately, e.g., for the purpose of crowdsourced data collection. Smartphones with their abundance and ubiquity are perfectly suited and have been widely used for crowdsourced data collection in real life settings. The ongoing, EC-funded research programme named BigO exploits the citizen-science paradigm to collect behavioral (eating, sleeping and physical activity) and affective (mood) data from children populations by means of a novel smartphone application with the intention of developing a decision support system to assist public health authorities in effective policy making against childhood obesity. This paper presents the development – in the context of BigO – of the myBigO app, one of the first citizen-scientist smartphone applications addressed to children for behavioral and affective data collection. This includes the design, implementation, and deployment of myBigO app in a number of data collection studies as well as its preliminary evaluation with respect to technical robustness and user experience in the context of these studies. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2020.

关键词： Decision support systems

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A Framework for Interpretability in Machine Learning for Medical Imaging

arXiv

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arXiv 2023年

作者： Wang, Alan Q. Karaman, Batuhan K. Kim, Heejong Rosenthal, Jacob Saluja, Rachit Young, Sean I. Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Cornell Tech New YorkNY10044 United States Department of Radiology Weill Cornell Medical School New YorkNY10065 United States Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New YorkNY10065 United States Martinos Center for Biomedical Imaging Harvard Medical School BostonMA02129 United States Computer Science and Artificial Intelligence Laboratory MIT CambridgeMA02139 United States

Interpretability for machine learning models in medical imaging (MLMI) is an important direction of research. However, there is a general sense of murkiness in what interpretability means. Why does the need for interpretability in MLMI arise? What goals does one actually seek to address when interpretability is needed? To answer these questions, we identify a need to formalize the goals and elements of interpretability in MLMI. By reasoning about real-world tasks and goals common in both medical image analysis and its intersection with machine learning, we identify five core elements of interpretability: localization, visual recognizability, physical attribution, model transparency, and actionability. From this, we arrive at a framework for interpretability in MLMI, which serves as a step-by-step guide to approaching interpretability in this context. Overall, this paper formalizes interpretability needs in the context of medical imaging, and our applied perspective clarifies concrete MLMI-specific goals and considerations in order to guide method design and improve real-world usage. Our goal is to provide practical and didactic information for model designers and practitioners, inspire developers of models in the medical imaging field to reason more deeply about what interpretability is achieving, and suggest future directions of interpretability research. © 2023, CC0.

关键词： Machine learning

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Multiple-Inputs Convolutional Neural Network for COVID-19 Classification and Critical Region Screening From Chest X-ray Radiographs: Model Development and Performance Evaluation

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JMIR Bioinformatics and Biotechnology 2022年第1期3卷 e36660页

作者： Li, Zhongqiang Li, Zheng Yao, Luke Chen, Qing Zhang, Jian Li, Xin Feng, Ji-Ming Li, Yanping Xu, Jian Division of Electrical and Computer Engineering College of Engineering Louisiana State University Baton Rouge LA United States Department of Visualization Texas A & M University College Station TX United States Department of Comparative Biomedical Science School of Veterinary Medicine Louisiana State University Baton Rouge LA United States School of Environment and Sustainability University of Saskatchewan Saskatoon SK Canada

Background: The COVID-19 pandemic is becoming one of the largest, unprecedented health crises, and chest X-ray radiography (CXR) plays a vital role in diagnosing COVID-19. However, extracting and finding useful image features from CXRs demand a heavy workload for radiologists. Objective: The aim of this study was to design a novel multiple-inputs (MI) convolutional neural network (CNN) for the classification of COVID-19 and extraction of critical regions from CXRs. We also investigated the effect of the number of inputs on the performance of our new MI-CNN model. Methods: A total of 6205 CXR images (including 3021 COVID-19 CXRs and 3184 normal CXRs) were used to test our MI-CNN models. CXRs could be evenly segmented into different numbers (2, 4, and 16) of individual regions. Each region could individually serve as one of the MI-CNN inputs. The CNN features of these MI-CNN inputs would then be fused for COVID-19 classification. More importantly, the contributions of each CXR region could be evaluated through assessing the number of images that were accurately classified by their corresponding regions in the testing data sets. Results: In both the whole-image and left-and right-lung region of interest (LR-ROI) data sets, MI-CNNs demonstrated good efficiency for COVID-19 classification. In particular, MI-CNNs with more inputs (2-, 4-, and 16-input MI-CNNs) had better efficiency in recognizing COVID-19 CXRs than the 1-input CNN. Compared to the whole-image data sets, the efficiency of LR-ROI data sets showed approximately 4% lower accuracy, sensitivity, specificity, and precision (over 91%). In considering the contributions of each region, one of the possible reasons for this reduced performance was that nonlung regions (eg, region 16) provided false-positive contributions to COVID-19 classification. The MI-CNN with the LR-ROI data set could provide a more accurate evaluation of the contribution of each region and COVID-19 classification. Additionally, the right-lung r

关键词： chest X-ray radiography COVID-19 multiple-inputs convolutional neural network screening critical COVID regions

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Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

arXiv

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arXiv 2022年

作者： Gan, Weijie Ying, Chunwei Eshraghi, Parna Wang, Tongyao Eldeniz, Cihat Hu, Yuyang Liu, Jiaming Chen, Yasheng An, Hongyu Kamilov, Ulugbek S. Department of Computer Science & Engineering Washington University in St. Louis St. Louis United States Department of Biomedical Engineering Washington University in St. Louis St. Louis United States Mallinckrodt Institute of Radiology Washington University in St. Louis St. Louis United States Department of Electrical & System Engineering Washington University in St. Louis St. Louis United States Department of Neurology Washington University in St. Louis St. Louis United States

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models—implicit neural networks with effectively infinite number of layers—were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Single Cell Investigative Genetics: Single-Cell Data Produces Genotype Distributions Concentrated at the True Genotype Across All Mixture Complexities

SSRN

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SSRN 2023年

作者： Grgicak, Catherine M. Bhembe, Qhawe Sheth, Nidhi C. Duffy, Ken R. Lun, Desmond S. Department of Chemistry Rutgers University CamdenNJ08102 United States Center for Computational and Integrative Biology Rutgers University CamdenNJ08102 United States Program in Biomedical Forensic Sciences Boston University BostonMA02118 United States Department of Mathematics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Hamilton Institute Maynooth University Ireland Department of Computer Science Rutgers University CamdenNJ08102 United States

In the absence of a suspect or PoI the forensic aim is investigative, and the focus is one of discerning what candidate genotypes best explain the evidence. In traditional bulk systems, the list of candidate genotypes may become vast if the sample contains DNA from many donors or the information from a minor contributor is swamped by that of major contributors, leading to lower evidential value for a true donor’s contribution and, as a result, possibly overlooked or inefficient investigative *** developments in single-cell analysis offer a way forward, by producing data capable of discriminating between genotypes. This is accomplished by first clustering single-cell data by similarity without reference to a known genotype. With good clustering it is reasonable to assume that the scEPGs in a cluster are of a single contributor. With that assumption we determine the probability of a cluster’s content given each possible genotype at each locus, which is then used to determine the posterior probability mass distribution for all genotypes by application of Bayes’ rule. A decision criterion is then applied such that the sum of the ranked probabilities of all genotypes falling in the set is at least . This is the credible genotype set and is the one used to inform database search criteria and, thus, investigative *** demonstrate the salience of single cell analysis by performance testing a set of 630 previously constructed admixtures containing up to 5 donors of balanced and unbalanced contributions. We use scEPGs that were generated by isolating single cells, employing a direct-to-PCR extraction treatment, amplifying STRs that are compliant with existing national databases and applying post-PCR treatments that elicit a detection limit of one DNA copy. We determined that, for these test data, 99.3% of the true genotypes are included in the 99.8% credible set, regardless of the number of donors that comprised the mixture. We also determined that the most proba

关键词： DNA

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Digital Health during COVID-19: Informatics Dialogue with the World Health Organization

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Yearbook of Medical Informatics 2021年第1期30卷 13-16页

作者： Koch, Sabine Hersh, William R. Bellazzi, Riccardo Leong, Tze Yun Yedaly, Moctar Al-Shorbaji, Najeeb Health Informatics Centre Department of Learning Informatics Management and Ethics Karolinska Institutet Stockholm Sweden Department of Medical Informatics & Clinical Epidemiology School of Medicine Oregon Health & Science University Portland OR United States Department of Electrical Computer and Biomedical Engineering University of Pavia Italy IRCCS ICS Maugeri Pavia Italy Department of Computer Science School of Computing National University of Singapore and AI Singapore Singapore Information Society Division African Union Commission Addis Ababa Ethiopia eHealth Development Association Amman Jordan

Background: On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic. Objectives: The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs). Methods: The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants’ consent and summarized in this paper. Results: The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general;(b) ethics and artificial intelligence;(c) digital divide;(d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic. Conclusions: Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly. © 2021 Thieme Medical Publishers, Inc.. All rights reserved.

关键词： Artificial Intelligence COVID-19 Global Health Medical Informatics World Health Organization

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Raman Spectroscopy Reveals Photobiomodulation-Induced α-Helix to β-Sheet Transition in Tubulins: Potential Implications for Alzheimer's and Other Neurodegenerative Diseases

arXiv

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arXiv 2023年

作者： Di Gregorio, Elisabetta Staelens, Michael Hosseinkhah, Nazanin Karimpoor, Mahroo Liburd, Janine Lim, Lew Shankar, Karthik Tuszyński, Jack A. Department of Physics Faculty of Science University of Alberta EdmontonABT6G 2E1 Canada Faculty of Biomedical Engineering Polytechnic University of Turin Turin10129 Italy Department of Physics Freie Universität Berlin Berlin14195 Germany Instituto de Física Corpuscular CSIC Universitat de València Carrer Catedràtic José Beltrán 2 València Paterna46980 Spain Vielight Inc. TorontoONM4Y 2G8 Canada Department of Electrical and Computer Engineering Faculty of Engineering University of Alberta EdmontonABT6G 1H9 Canada Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland

In small clinical studies, the application of photobiomodulation (PBM), which typically delivers near-infrared (NIR) to treat the brain, has presented some remarkable results in the treatment of dementia and several neurodegenerative diseases. However, while the literature is rich with the mechanisms of action underlying PBM outcomes, the underlying mechanisms affecting a neurodegenerative disease are not entirely clear. While large clinical trials are warranted to validate these findings, evidence of the mechanisms can explain and hence provide credible support for PBM as a potential treatment for these diseases. Tubulin and its polymerized state of microtubules have been known to play important roles in the pathology of Alzheimer's and other neurodegenerative diseases. We investigated the effects of PBM on these structures in the quest for answers. In this study, we employed a Raman spectroscopic analysis of the amide I band of polymerized samples of tubulin exposed to pulsed low-intensity NIR radiation (810 nm, 10 Hz, 22.5 J/cm2 dose). Peaks in the Raman fingerprint region (300-1900 cm−1), in particular, in the amide I band (1600-1700 cm−1), can be used to quantify the percentage of protein secondary structures. Under this band, hidden signals of C=O stretching, belonging to different structures, are superimposed-producing a complex signal as a result. An accurate decomposition of the amide I band is therefore required for the reliable analysis of the conformation of proteins, which we achieved through a straightforward method employing a Voigt profile. This approach was validated through secondary structure analyses of unexposed control samples, for which comparisons with other values available in the literature could be conducted. Subsequently, using this validated method, we present novel findings of statistically significant alterations in the secondary structures of NIR-exposed tubulin, characterized by a notable decrease in α-helix content and a concurrent

关键词： Neurodegenerative diseases

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ResNet Structure Simplification with the Convolutional Kernel Redundancy Measure

arXiv

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arXiv 2022年

作者： Zhu, Hongzhi Rohling, Robert Salcudean, Septimiu Department of Electrical and Computer Engineering University of British Columbia 5500-2332 Main Mall VancouverBCV6T 1Z4 Canada School of Biomedical Engineering University of British Columbia 251-2222 Health Sciences Mall VancouverBCV6T 1Z4 Canada Department of Mechanical Engineering University of British Columbia 2054-6250 Applied Science Lane VancouverBCV6T 1Z4 Canada

Deep learning, especially convolutional neural networks, has triggered accelerated advancements in computer vision, bringing changes into our daily practice. Furthermore, the standardized deep learning modules (also known as backbone networks), i.e., ResNet and EfficientNet, have enabled efficient and rapid development of new computer vision solutions. Yet, deep learning methods still suffer from several drawbacks. One of the most concerning problems is the high memory and computational cost, such that dedicated computing units, typically GPUs, have to be used for training and development. Therefore, in this paper, we propose a quantifiable evaluation method, the convolutional kernel redundancy measure, which is based on perceived image differences, for guiding the network structure simplification. When applying our method to the chest X-ray image classification problem with ResNet, our method can maintain the performance of the network and reduce the number of parameters from over 23 million to approximately 128 thousand (reducing 99.46% of the parameters). © 2022, CC BY-NC-SA.

关键词： Redundancy

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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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