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Development and Validation of a Multimodal-Based Prognosis and Intervention Prediction Model for COVID-19 Patients in a Multicenter Cohort

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Author(s)
Jeong Hoon LeeJong Seok AhnMyung Jin ChungYeon Joo JeongJin Hwan KimJae Kwang LimJin Young KimYoung Jae KimJong Eun LeeEun Young Kim
Keimyung Author(s)
Kim, Jin Young
Department
Dept. of Radiology (영상의학)
Journal Title
Sensors (Basel)
Issued Date
2022
Volume
22
Issue
13
Keyword
COVID-19artificial intelligenceprognosischest radiograph
Abstract
The ability to accurately predict the prognosis and intervention requirements for treating highly infectious diseases, such as COVID-19, can greatly support the effective management of patients, especially in resource-limited settings. The aim of the study is to develop and validate a multimodal artificial intelligence (AI) system using clinical findings, laboratory data and AI-interpreted features of chest X-rays (CXRs), and to predict the prognosis and the required interventions for patients diagnosed with COVID-19, using multi-center data. In total, 2282 real-time reverse transcriptase polymerase chain reaction-confirmed COVID-19 patients’ initial clinical findings, laboratory data and CXRs were retrospectively collected from 13 medical centers in South Korea, between January 2020 and June 2021. The prognostic outcomes collected included intensive care unit (ICU) admission and in-hospital mortality. Intervention outcomes included the use of oxygen (O2) supplementation, mechanical ventilation and extracorporeal membrane oxygenation (ECMO). A deep learning algorithm detecting 10 common CXR abnormalities (DLAD-10) was used to infer the initial CXR taken. A random forest model with a quantile classifier was used to predict the prognostic and intervention outcomes, using multimodal data. The area under the receiver operating curve (AUROC) values for the single-modal model, using clinical findings, laboratory data and the outputs from DLAD-10, were 0.742 (95% confidence interval [CI], 0.696–0.788), 0.794 (0.745–0.843) and 0.770 (0.724–0.815), respectively. The AUROC of the combined model, using clinical findings, laboratory data and DLAD-10 outputs, was significantly higher at 0.854 (0.820–0.889) than that of all other models (p < 0.001, using DeLong’s test). In the order of importance, age, dyspnea, consolidation and fever were significant clinical variables for prediction. The most predictive DLAD-10 output was consolidation. We have shown that a multimodal AI model can improve the performance of predicting both the prognosis and intervention in COVID-19 patients, and this could assist in effective treatment and subsequent resource management. Further, image feature extraction using an established AI engine with well-defined clinical outputs, and combining them with different modes of clinical data, could be a useful way of creating an understandable multimodal prediction model.
Keimyung Author(s)(Kor)
김진영
Publisher
School of Medicine (의과대학)
Type
Article
ISSN
1424-8220
Source
https://www.mdpi.com/1424-8220/22/13/5007
DOI
10.3390/s22135007
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44404
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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