Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT
You-Zhen Feng,
Sidong Liu,
Zhong-Yuan Cheng,
Juan C. Quiroz,
Dana Rezazadegan,
Ping-Kang Chen,
Qi-Ting Lin,
Long Qian,
Xiao-Fang Liu,
Shlomo Berkovsky,
Enrico Coiera,
Lei Song,
Xiao-Ming Qiu,
Xiang-Ran Cai
Affiliations
You-Zhen Feng
Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Sidong Liu
Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia
Zhong-Yuan Cheng
Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Juan C. Quiroz
Centre for Big Data Research in Health, University of New South Wales, Sydney 1466, Australia
Dana Rezazadegan
Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne 3000, Australia
Ping-Kang Chen
Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Qi-Ting Lin
Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Long Qian
Department of Biomedical Engineering, Peking University, Beijing 100871, China
Xiao-Fang Liu
Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Artificial Intelligence, Nankai University, Tianjin 300350, China
Shlomo Berkovsky
Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia
Enrico Coiera
Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney 2113, Australia
Lei Song
Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441003, China
Xiao-Ming Qiu
Department of Radiology, Huangshi Central Hospital, Affiliated Hospital of Hubei Polytechnic University, Edong Healthcare Group, Huangshi 435002, China
Xiang-Ran Cai
Medical Imaging Centre, The First Affiliated Hospital of Jinan University, Guangzhou 510630, China
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.
