{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:30:15Z","timestamp":1787164215727,"version":"build-2736575974"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100024075","name":"Region Syddanmark","doi-asserted-by":"publisher","award":["10240300"],"award-info":[{"award-number":["10240300"]}],"id":[{"id":"10.13039\/501100024075","id-type":"DOI","asserted-by":"publisher"}]},{"name":"University Library of Southern Denmark"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>\n                      All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24\u00a0months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AI\n                      <jats:sub>sens<\/jats:sub>\n                      ) and specificity (AI\n                      <jats:sub>spec<\/jats:sub>\n                      ) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AI\n                      <jats:sub>sens<\/jats:sub>\n                      had lower specificity (97.5% vs 97.7%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) and PPV (17.5% vs 18.7%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.01) and a higher RR (3.0% vs 2.8%;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) than first readers. AI\n                      <jats:sub>spec<\/jats:sub>\n                      was comparable to first readers in terms of all accuracy measures. Both AI\n                      <jats:sub>sens<\/jats:sub>\n                      and AI\n                      <jats:sub>spec<\/jats:sub>\n                      detected significantly fewer screen-detected cancers (1166 (AI\n                      <jats:sub>sens<\/jats:sub>\n                      ), 1156 (AI\n                      <jats:sub>spec<\/jats:sub>\n                      ) vs 1252;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) but found more interval cancers compared to first readers (126 (AI\n                      <jats:sub>sens<\/jats:sub>\n                      ), 117 (AI\n                      <jats:sub>spec<\/jats:sub>\n                      ) vs 39;\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.0001) with varying types of cancers detected across multiple subgroups.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance statement<\/jats:title>\n                    <jats:p>Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Standalone AI cancer detection was compared to first readers in a double-read mammography screening population.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-023-10423-7","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T01:01:52Z","timestamp":1699405312000},"page":"3935-3946","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms"],"prefix":"10.1007","volume":"34","author":[{"given":"Johanne","family":"K\u00fchl","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-2352","authenticated-orcid":false,"given":"Mohammad Talal","family":"Elhakim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah Wordenskjold","family":"Stougaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benjamin Schnack Brandt","family":"Rasmussen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mads","family":"Nielsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oke","family":"Gerke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lisbet Br\u00f8nsro","family":"Larsen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ole","family":"Graumann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"10423_CR1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoncol.2016.5688","author":"C Fitzmaurice","year":"2017","unstructured":"Fitzmaurice C, Allen C, Barber RM et al (2017) Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life-years for 32 cancer groups, 1990 to 2015: a systematic analysis for the global burden of disease study. 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Hence, the index test, study sample, and subsequently, all results differ from the current study. Furthermore, the methodology in terms of the testing of the index test also differs.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 diagnostic or prognostic study\u2022 multicenter study","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}