Clinical longitudinal evaluation of COVID-19 patients and prediction of organ-specific recovery using artificial intelligence

作     者：Winston T.Wang Charlotte L.Zhang Kang Wei Ye Sang Jun Shen Guangyu Wang Alexander X.Lozano 

作者机构：Department of Materials Science&EngineeringStanford UniversityStanfordCA 94305USA Mayo Clinic Alix school of MedicineScottsdaleAZ 85259USA Guangzhou Women and Children’s Medical CenterGuangzhou Medical UniversityGuangzhou 510623China The First College of Clinical Medical ScienceChina Three Gorges UniversityYichang 443000China Department of RadiologySun Yat-Sen Memorial HospitalSun Yat-Sen UniversityGuangzhou 510120China School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijing 100876China 

出 版 物：《Precision Clinical Medicine》 (精准临床医学（英文）)

年 卷 期：2021年第4卷第1期

页      面：62-69页

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：This study was funded by the National Natural Science Foundation of China(Grant No.61906105) 

主　　题：artificial intelligence medical image COVID-19 longitudinal evaluation 

摘      要：Within COVID-19 there is an urgent unmet need to predict at the time of hospital admission which COVID-19 patients will recover from the disease,and how fast they recover to deliver personalized treatments and to properly allocate hospital resources so that healthcare systems do not become *** this end,we have combined clinically salient CT imaging data synergistically with laboratory testing data in an integrative machine learning model to predict organ-specific recovery of patients from *** trained and validated our model in 285 patients on each separate major organ system impacted by COVID-19 including the renal,pulmonary,immune,cardiac,and hepatic *** greatly enhance the speed and utility of our model,we applied an artificial intelligence method to segment and classify regions on CT imaging,from which interpretable data could be directly fed into the predictive machine learning model for overall *** all organ systems we achieved validation set area under the receiver operator characteristic curve(AUC)values for organ-specific recovery ranging from 0.80 to 0.89,and significant overall recovery prediction in Kaplan-Meier *** demonstrates that the synergistic use of an artificial intelligence(AI)framework applied to CT lung imaging and a machine learning model that integrates laboratory test data with imaging data can accurately predict the overall recovery of COVID-19 patients from baseline characteristics.