{"id":1553,"date":"2023-05-16T20:11:25","date_gmt":"2023-05-16T20:11:25","guid":{"rendered":"https:\/\/www.lunit.io\/publication\/effect-of-artificial-intelligence-based-computer-aided-diagnosis-on-the-screening-outcomes-of-digital-mammography-a-matched-cohort-study\/"},"modified":"2025-11-02T03:23:08","modified_gmt":"2025-11-02T03:23:08","slug":"effect-of-artificial-intelligence-based-computer-aided-diagnosis-on-the-screening-outcomes-of-digital-mammography-a-matched-cohort-study","status":"publish","type":"publication","link":"https:\/\/lunit.supremeclients.com\/ko\/publication\/effect-of-artificial-intelligence-based-computer-aided-diagnosis-on-the-screening-outcomes-of-digital-mammography-a-matched-cohort-study\/","title":{"rendered":"Effect of artificial intelligence\u2013based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study"},"content":{"rendered":"<h3>Effect of artificial intelligence\u2013based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study<\/h3>\n<p>Haejung Kim, Ji Soo Choi, Kyunga Kim, Eun Sook Ko, Eun Young Ko &amp; Boo-Kyung Han<\/p>\n<p><strong>European Radiology, 2023<\/strong><\/p>\n<p><strong>Abstract<\/strong><br \/>\n<strong>Objective<\/strong><br \/>\nTo investigate whether artificial intelligence\u2013based computer-aided diagnosis (AI-CAD) can improve radiologists\u2019 performance when used to support radiologists\u2019 interpretation of digital mammography (DM) in breast cancer screening.<\/p>\n<p><strong>Methods<\/strong><br \/>\nA retrospective database search identified 3158 asymptomatic Korean women who consecutively underwent screening DM between January and December 2019 without AI-CAD support, and screening DM between February and July 2020 with image interpretation aided by AI-CAD in a tertiary referral hospital using single reading. Propensity score matching was used to match the DM with AI-CAD group in a 1:1 ratio with the DM without AI-CAD group according to age, breast density, experience level of the interpreting radiologist, and screening round. Performance measures were compared with the McNemar test and generalized estimating equations.<\/p>\n<p><strong>Results<\/strong><br \/>\nA total of 1579 women who underwent DM with AI-CAD were matched with 1579 women who underwent DM without AI-CAD. Radiologists showed higher specificity (96% [1500 of 1563] vs 91.6% [1430 of 1561]; p\u2009&lt;\u20090.001) and lower abnormal interpretation rates (AIR) (4.9% [77 of 1579] vs 9.2% [145 of 1579]; p\u2009&lt;\u20090.001) with AI-CAD than without. There was no significant difference in the cancer detection rate (CDR) (AI-CAD vs no AI-CAD, 8.9 vs 8.9 per 1000 examinations; p\u2009=\u20090.999), sensitivity (87.5% vs 77.8%; p\u2009=\u20090.999), and positive predictive value for biopsy (PPV3) (35.0% vs 35.0%; p\u2009=\u20090.999) according to AI-CAD support.<\/p>\n<p><strong>Conclusions<\/strong><br \/>\nAI-CAD increases the specificity for radiologists without decreasing sensitivity as a supportive tool in the single reading of DM for breast cancer screening.<\/p>\n<p style=\"text-align: center;\"><a href=\"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-09692-z\"><strong>Read the full paper<\/strong><\/a><\/p>\n","protected":false},"featured_media":0,"template":"","publication-oncology":[],"publication-region":[87],"publication-type":[],"radiology":[97,98,96],"class_list":["post-1553","publication","type-publication","status-publish","hentry","publication-region-asia","radiology-breast","radiology-enhancing-workflow-efficiency","radiology-lunit-insight"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - 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