Abstract
Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.
Keywords: Coronavirus Disease 2019 (COVID-19); artificial intelligence; biomedical imaging application; chest CT image; imaging biomarker; multicentric retrospective study.
【저자키워드】 artificial intelligence, Coronavirus disease 2019 (COVID-19), Chest CT image, imaging biomarker, multicentric retrospective study., biomedical imaging application, 【초록키워드】 COVID-19, Treatment, Biomarker, Pneumonia, hospital, global pandemic, Retrospective study, Chest CT, Accuracy, Chest, Community, patients, COVID-19 patients, COVID-19 patient, key factor, independent, healthy, significantly higher, assist, 【제목키워드】 COVID-19, Biomarker, Chest, development, early stage,