乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T23:18:09Z","timestamp":1726355889940},"reference-count":53,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T00:00:00Z","timestamp":1636934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"Automatic severity assessment and progression prediction can facilitate admission, triage, and referral of COVID-19 patients. This study aims to explore the potential use of lung lesion features in the management of COVID-19, based on the assumption that lesion features may carry important diagnostic and prognostic information for quantifying infection severity and forecasting disease progression. A novel LesionEncoder framework is proposed to detect lesions in chest CT scans and to encode lesion features for automatic severity assessment and progression prediction. The LesionEncoder framework consists of a U-Net module for detecting lesions and extracting features from individual CT slices, and a recurrent neural network (RNN) module for learning the relationship between feature vectors and collectively classifying the sequence of feature vectors. Chest CT scans of two cohorts of COVID-19 patients from two hospitals in China were used for training and testing the proposed framework. When applied to assessing severity, this framework outperformed baseline methods achieving a sensitivity of 0.818, specificity of 0.952, accuracy of 0.940, and AUC of 0.903. It also outperformed the other tested methods in disease progression prediction with a sensitivity of 0.667, specificity of 0.838, accuracy of 0.829, and AUC of 0.736. The LesionEncoder framework demonstrates a strong potential for clinical application in current COVID-19 management, particularly in automatic severity assessment of COVID-19 patients. This framework also has a potential for other lesion-focused medical image analyses.<\/jats:p>","DOI":"10.3390\/info12110471","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T01:46:47Z","timestamp":1637027207000},"page":"471","source":"Crossref","is-referenced-by-count":7,"title":["Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT"],"prefix":"10.3390","volume":"12","author":[{"given":"You-Zhen","family":"Feng","sequence":"first","affiliation":[{"name":"Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China"}]},{"given":"Sidong","family":"Liu","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia"}]},{"given":"Zhong-Yuan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0241-5376","authenticated-orcid":false,"given":"Juan C.","family":"Quiroz","sequence":"additional","affiliation":[{"name":"Centre for Big Data Research in Health, University of New South Wales, Sydney 1466, Australia"}]},{"given":"Dana","family":"Rezazadegan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3000, Australia"}]},{"given":"Ping-Kang","family":"Chen","sequence":"additional","affiliation":[{"name":"Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China"}]},{"given":"Qi-Ting","family":"Lin","sequence":"additional","affiliation":[{"name":"Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China"}]},{"given":"Long","family":"Qian","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Peking University, Beijing 100871, China"}]},{"given":"Xiao-Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}]},{"given":"Shlomo","family":"Berkovsky","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia"}]},{"given":"Enrico","family":"Coiera","sequence":"additional","affiliation":[{"name":"Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia"}]},{"given":"Lei","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441003, China"}]},{"given":"Xiao-Ming","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi 435002, China"}]},{"given":"Xiang-Ran","family":"Cai","sequence":"additional","affiliation":[{"name":"Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e480","DOI":"10.1016\/S2214-109X(20)30068-1","article-title":"Potential association between COVID-19 mortality and health-care resource availability","volume":"8","author":"Ji","year":"2020","journal-title":"Lancet Glob. 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