TY - JOUR T1 - Deep-learning algorithm to detect fibrosing interstitial lung disease on chest radiographs JF - European Respiratory Journal JO - Eur Respir J DO - 10.1183/13993003.02269-2021 VL - 61 IS - 2 SP - 2102269 AU - Hirotaka Nishikiori AU - Koji Kuronuma AU - Kenichi Hirota AU - Naoya Yama AU - Tomohiro Suzuki AU - Maki Onodera AU - Koichi Onodera AU - Kimiyuki Ikeda AU - Yuki Mori AU - Yuichiro Asai AU - Yuzo Takagi AU - Seiwa Honda AU - Hirofumi Ohnishi AU - Masamitsu Hatakenaka AU - Hiroki Takahashi AU - Hirofumi Chiba Y1 - 2023/02/01 UR - http://erj.ersjournals.com/content/61/2/2102269.abstract N2 - 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 is challenging. In this study, we developed and tested a deep-learning algorithm to detect CF-ILD using chest radiograph images.Method From the image archive of Sapporo Medical University Hospital, 653 chest radiographs 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 receiver operating characteristic curve, which indicates the algorithm's detection capability, was 0.979. Using a score cut-off 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 noninferior 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 chest radiograph images. The algorithm's detection capability was noninferior to that of doctors.A deep-learning algorithm was developed to detect fibrotic interstitial lung disease using chest radiographs. The algorithm's detection capability was noninferior to that of doctors, including pulmonologists and radiologists. https://bit.ly/3SAClW2 ER -