Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets

Harmon, Stephanie A.; Sanford, Thomas H.; Xu, Sheng; Turkbey, Evrim B.; Roth, Holger; Xu, Ziyue; Yang, Dong; Myronenko, Andriy; Anderson, Victoria; Amalou, Amel; Blain, Maxime; Kassin, Michael; Long, Dilara; Varble, Nicole; Walker, Stephanie M.; Bagci, Ulas; Ierardi, Anna Maria; Stellato, Elvira; Plensich, Guido Giovanni; Franceschelli, Giuseppe; Girlando, Cristiano; Irmici, Giovanni; Labella, Dominic; Hammoud, Dima; Malayeri, Ashkan; Jones, Elizabeth; Summers, Ronald M.; Choyke, Peter L.; Xu, Daguang; Flores, Mona; Tamura, Kaku; Obinata, Hirofumi; Mori, Hitoshi; Patella, Francesca; Cariati, Maurizio; Carrafiello, Gianpaolo; An, Peng; Wood, Bradford J.; Turkbey, Baris

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.