Dataset Information

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

ABSTRACT:

Background

An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.

Objective

We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.

Methods

This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.

Results

The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).

Conclusions

The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

SUBMITTER: Lee HW

PROVIDER: S-EPMC9937110 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Publications

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

Lee Hyun Woo HW Yang Hyun Jun HJ Kim Hyungjin H Kim Ue-Hwan UH Kim Dong Hyun DH Yoon Soon Ho SH Ham Soo-Youn SY Nam Bo Da BD Chae Kum Ju KJ Lee Dabee D Yoo Jin Young JY Bak So Hyeon SH Kim Jin Young JY Kim Jin Hwan JH Kim Ki Beom KB Jung Jung Im JI Lim Jae-Kwang JK Lee Jong Eun JE Chung Myung Jin MJ Lee Young Kyung YK Kim Young Seon YS Lee Sang Min SM Kwon Woocheol W Park Chang Min CM Kim Yun-Hyeon YH Jeong Yeon Joo YJ Jin Kwang Nam KN Goo Jin Mo JM

Journal of medical Internet research 20230216

<h4>Background</h4>An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.<h4>Objective</h4>We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.<h4>Methods</h4>This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 202 ...[more]

PMID: 36795468

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Dataset Information

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

Background

Objective

Methods

Results

Conclusions

Publications

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study.

Similar Datasets

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