{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:19:27Z","timestamp":1790036367374,"version":"4.0.1"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>The purpose of this study is to assess the \u201creal-world\u201d impact of an artificial intelligence (AI) tool designed to detect breast cancer in digital breast tomosynthesis (DBT) screening exams following 12 months of utilization in a subspecialized academic breast center.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following IRB approval, mammography audit reports, as specified in the BI-RADS atlas, were retrospectively generated for five radiologists reading at three locations during a 12-month time frame. One location had the AI tool (iCAD ProFound AI v2.0), and the other two locations did not. The co-primary endpoints were cancer detection rate (CDR) and abnormal interpretation rate (AIR). Secondary endpoints included positive predictive values (PPVs) for cancer among screenings with abnormal interpretations (PPV1) and for biopsies performed (PPV3). Odds ratios (OR) with two-sided 95% confidence intervals (CIs) summarized the impact of AI across radiologists using generalized estimating equations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Nonsignificant differences were observed in CDR, AIR, and PPVs. The CDR was 7.3 with AI and 5.9 without AI (OR 1.3, 95% CI: 0.9\u20131.7). The AIR was 11.7% with AI and 11.8% without AI (OR 1.0, 95% CI: 0.8\u20131.3). The PPV1 was 6.2% with AI and 5.0% without AI (OR 1.3, 95% CI: 0.97\u20131.7). The PPV3 was 33.3% with AI and 32.0% without AI (OR 1.1, 95% CI: 0.8\u20131.5).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Although we are unable to show statistically significant changes in CDR and AIR outcomes in the two groups, the results are consistent with prior reader studies. There is a nonsignificant trend toward improvement in CDR with AI, without significant increases in AIR.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jbi\/wbad015","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T16:11:38Z","timestamp":1680279098000},"page":"258-266","source":"Crossref","is-referenced-by-count":19,"title":["Use of Artificial Intelligence for Digital Breast Tomosynthesis Screening: A Preliminary Real-world Experience"],"prefix":"10.1093","volume":"5","author":[{"given":"Haley","family":"Letter","sequence":"first","affiliation":[{"name":"Mayo Clinic, Department of Radiology , Jacksonville, FL , USA"},{"name":"University of Florida, Department of Radiology , Jacksonville, FL , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meridith","family":"Peratikos","sequence":"additional","affiliation":[{"name":"Biostatistics Consulting LLC , Kensington, MD , 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