Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity, as evaluated in an independent test set (not included in training and validation) of 1337 patients. Normal controls included chest CTs from oncology, emergency, and pneumonia-related indications. The false positive rate in 140 patients with laboratory confirmed other (non COVID-19) pneumonias was 10%. AI-based algorithms can readily identify CT scans with COVID-19 associated pneumonia, as well as distinguish non-COVID related pneumonias with high specificity in diverse patient populations. Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Here, the authors present a multinational study on the application of deep learning algorithms for COVID-19 diagnosis against multiple lung conditions as controls.
【저자키워드】 Infectious diseases, Computer science, 【초록키워드】 COVID-19, deep learning, Pneumonia, artificial intelligence, diagnostic, lung, Lung disease, Laboratory, sensitivity, specificity, Cohort, Chest CT, Accuracy, CT scan, Algorithm, Patient, COVID-19 diagnosis, Control, Clinical management, patients, Algorithms, oncology, indications, populations, controls, independent, false positive rate, clinical entities, normal, identify, condition, with COVID-19, 【제목키워드】 COVID-19, Pneumonia, artificial intelligence, Chest CT, dataset,