{"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/en/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–based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study"},"content":{"rendered":"<h3>Effect of artificial intelligence–based 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–based computer-aided diagnosis (AI-CAD) can improve radiologists’ performance when used to support radiologists’ 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 &lt; 0.001) and lower abnormal interpretation rates (AIR) (4.9% [77 of 1579] vs 9.2% [145 of 1579]; p &lt; 0.001) 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 = 0.999), sensitivity (87.5% vs 77.8%; p = 0.999), and positive predictive value for biopsy (PPV3) (35.0% vs 35.0%; p = 0.999) 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 v27.6 - 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