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In The European respiratory journal

BACKGROUND : Antifibrotic therapies are available to treat chronic fibrosing interstitial lung diseases (CF-ILDs), including idiopathic pulmonary fibrosis. Early use of these treatments is recommended to slow deterioration of respiratory function and to prevent acute exacerbation. However, identifying patients in the early stages of CF-ILD using chest radiographs (CRs) is challenging. In this study, we developed and tested a deep learning algorithm to detect CF-ILD using CR images.

METHOD : From the image archive of Sapporo Medical University Hospital, 653 CRs from 263 patients with CF-ILDs and 506 from 506 patients without CF-ILD were identified; 921 were used for deep learning and 238 were used for algorithm testing. The algorithm was designed to output a numerical score ranging from 0 to 1, representing the probability of CF-ILD. Using the testing dataset, the algorithm's capability to identify CF-ILD was compared with that of doctors. A second dataset, in which CF-ILD was confirmed using computed tomography images, was used to further evaluate the algorithm's performance.

RESULTS : The area under the curve of the receiver operating characteristic curve, which indicates the algorithm's detection capability, was 0.979. Using a score cutoff of 0.267, the sensitivity and specificity of detection were 0.896 and 1.000, respectively. These data showed that the algorithm's performance was non-inferior to that of doctors, including pulmonologists and radiologists; performance was verified using the second dataset.

CONCLUSIONS : We developed a deep learning algorithm to detect CF-ILDs using CR images. The algorithm's detection capability was non-inferior to that of doctors.

Nishikiori Hirotaka, Kuronuma Koji, Hirota Kenichi, Yama Naoya, Suzuki Tomohiro, Onodera Maki, Onodera Koichi, Ikeda Kimiyuki, Mori Yuki, Asai Yuichiro, Takagi Yuzo, Honda Seiwa, Ohnishi Hirofumi, Hatakenaka Masamitsu, Takahashi Hiroki, Chiba Hirofumi