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In Nature communications ; h5-index 260.0

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.

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