{"id":1604,"date":"2021-05-20T20:11:33","date_gmt":"2021-05-20T20:11:33","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\/"},"modified":"2025-11-02T03:04:43","modified_gmt":"2025-11-02T03:04:43","slug":"development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs","status":"publish","type":"publication","link":"https:\/\/www.lunit.io\/en\/publication\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\/","title":{"rendered":"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs"},"content":{"rendered":"<h3>Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs<\/h3>\n<p>Ju Gang Nam, Minchul Kim, Jongchan Park, et al.<\/p>\n<p><strong>European Respiratory Journal, 2020<\/strong><\/p>\n<p><strong>Abstract<\/strong><br \/>\nWe 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.DLAD-10 was trained with 146 717 radiographs from 108 053 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.DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895-1.00 in the CT-confirmed dataset and 0.913-0.997 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) versus 29.2% (7\/24); p=0.006) and urgent (82.7% (258\/312) versus 78.2% (244\/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean\u00b1sd time-to-report critical and urgent radiographs (640.5\u00b1466.3 versus 3371.0\u00b11352.5 s and 1840.3\u00b11141.1 versus 2127.1\u00b11468.2 s, respectively; all p&lt;0.01) and reduced the mean\u00b1sd interpretation time (20.5\u00b122.8 versus 23.5\u00b123.7 s; p&lt;0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/33243843\/\"><strong>Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[87],"publication-type":[],"radiology":[104,106,96],"class_list":["post-1604","publication","type-publication","status-publish","hentry","publication-region-asia","radiology-chest","radiology-improving-accuracy","radiology-lunit-insight"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs - Lunit<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.lunit.io\/en\/publication\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs - Lunit\" \/>\n<meta property=\"og:description\" content=\"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs Ju Gang Nam, Minchul Kim, Jongchan Park, et al. European Respiratory Journal, 2020 Abstract 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 [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.lunit.io\/en\/publication\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\/\" \/>\n<meta property=\"og:site_name\" content=\"Lunit\" \/>\n<meta property=\"article:modified_time\" content=\"2025-11-02T03:04:43+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@lunit_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\\\/\",\"name\":\"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs - Lunit\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#website\"},\"datePublished\":\"2021-05-20T20:11:33+00:00\",\"dateModified\":\"2025-11-02T03:04:43+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/publication\\\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#website\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/\",\"name\":\"Lunit\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#organization\",\"name\":\"Lunit\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/Logo-black.svg\",\"contentUrl\":\"https:\\\/\\\/www.lunit.io\\\/en\\\/wp-content\\\/uploads\\\/2025\\\/10\\\/Logo-black.svg\",\"width\":189,\"height\":52,\"caption\":\"Lunit\"},\"image\":{\"@id\":\"https:\\\/\\\/www.lunit.io\\\/ko\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/lunit_ai\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/lunit-inc\",\"https:\\\/\\\/x.com\\\/lunitoncology\"]}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs - Lunit","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/www.lunit.io\/en\/publication\/development-and-validation-of-a-deep-learning-algorithm-detecting-10-common-abnormalities-on-chest-radiographs\/","og_locale":"en_US","og_type":"article","og_title":"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs - Lunit","og_description":"Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs Ju Gang Nam, Minchul Kim, Jongchan Park, et al. 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