{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T16:17:44Z","timestamp":1792599464741,"version":"4.5.0"},"reference-count":29,"publisher":"European Respiratory Society (ERS)","issue":"5","license":[{"start":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T00:00:00Z","timestamp":1606348800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["grant number: NRF-2018R1A5A1060031"],"award-info":[{"award-number":["grant number: NRF-2018R1A5A1060031"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004332","name":"Seoul National University Hospital","doi-asserted-by":"publisher","award":["grant number: 03-2019-0190"],"award-info":[{"award-number":["grant number: 03-2019-0190"]}],"id":[{"id":"10.13039\/501100004332","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir J"],"accepted":{"date-parts":[[2020,11,3]]},"published-print":{"date-parts":[[2021,5]]},"abstract":"<jats:p>We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy.<\/jats:p><jats:p>DLAD-10 was trained with 146\u200a717 radiographs from 108\u200a053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule\/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day computed tomography (CT)-confirmed dataset (normal:abnormal 53:147) and an open-source dataset (PadChest; normal:abnormal 339:334) was compared with that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent and 146 nonurgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.<\/jats:p><jats:p>DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895\u20131.00 in the CT-confirmed dataset and 0.913\u20130.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% (57\/60)) than pooled radiologists (84.4% (152\/180); p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% (17\/24)<jats:italic>versus<\/jats:italic>29.2% (7\/24); p=0.006) and urgent (82.7% (258\/312)<jats:italic>versus<\/jats:italic>78.2% (244\/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean\u00b1<jats:sc>sd<\/jats:sc>time-to-report critical and urgent radiographs (640.5\u00b1466.3<jats:italic>versus<\/jats:italic>3371.0\u00b11352.5\u2005s and 1840.3\u00b11141.1<jats:italic>versus<\/jats:italic>2127.1\u00b11468.2\u2005s, respectively; all p&lt;0.01) and reduced the mean\u00b1<jats:sc>sd<\/jats:sc>interpretation time (20.5\u00b122.8<jats:italic>versus<\/jats:italic>23.5\u00b123.7\u2005s; p&lt;0.001).<\/jats:p><jats:p>DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.<\/jats:p>","DOI":"10.1183\/13993003.03061-2020","type":"journal-article","created":{"date-parts":[[2020,11,26]],"date-time":"2020-11-26T22:10:29Z","timestamp":1606428629000},"page":"2003061","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":95,"title":["Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs"],"prefix":"10.1183","volume":"57","author":[{"given":"Ju Gang","family":"Nam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minchul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jongchan","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eui Jin","family":"Hwang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong Hyuk","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung Hee","family":"Hong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin Mo","family":"Goo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang Min","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"81","published-online":{"date-parts":[[2020,11,26]]},"reference":[{"key":"2024102102071834000_57.5.2003061.1","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1148\/radiol.2020192256","article-title":"Patient exposure from radiologic and nuclear medicine procedures in the United States: procedure volume and effective dose for the period 2006\u20132016","volume":"295","author":"Mettler","year":"2020","journal-title":"Radiology"},{"key":"2024102102071834000_57.5.2003061.2","unstructured":"United Nations Scientific Committee on the Effects of Atomic Radiation. 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