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Predicting the Need for Intensive Care for COVID-19 Patients using Deep Learning on Chest Radiography
Qiyuan Hu

In this study, we propose an artificial intelligent (AI) COVID-19 prognosis method to predict patients’ needs for intensive care by analyzing chest radiography (CXR) images using deep learning. The dataset consisted of the CXR exams of 1178 COVID-19 positive patients as confirmed by reverse transcription polymerase chain reaction tests for the SARS-CoV-2 virus, 20% of which were held out for testing. Our model was based on DenseNet121 and a curriculum learning technique was employed to train on a sequence of gradually more specific and complex tasks: 1) fine-tuning a model pretrained on ImageNet using a previously established CXR dataset with a broad spectrum of pathologies, 2) refining on another established dataset to detect pneumonia, and 3) fine-tuning on our training/validation dataset to predict patients’ needs for intensive care within 24, 48, 72, and 96 hours following the CXR exams. The classification performances were evaluated on the independent test set using the area under the receiver operating characteristic curve (AUC) as the performance metric in the task of distinguishing between those COVID-19-positive patients who required intensive care and those who did not. We achieved an AUC [95% confidence interval] of 0.77 [0.70, 0.84] when predicting the need for intensive care 24 hours in advance, and at least 0.73 [0.66, 0.80] for earlier predictions based on the AI prognostic marker derived from CXR images.

Author Information

Qiyuan Hu (University of Chicago)

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