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Artificial intelligence (AI) might be a solution. The aim of this study was to retrospectively test different ways of using AI in a screening workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>An AI system was used to analyse 14,772 double-read single-view DBT examinations from a screening trial with paired DM double reading. Three scenarios were studied: if AI can identify normal cases that can be excluded from human reading; if AI can replace the second reader; if AI can replace both readers. The number of detected cancers and false positives was compared with DM or DBT double reading.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      By excluding normal cases and only reading 50.5% (7460\/14,772) of all examinations, 95% (121\/127) of the DBT double reading detected cancers could be detected. Compared to DM screening, 27% (26\/95) more cancers could be detected (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001) while keeping recall rates at the same level. With AI replacing the second reader, 95% (120\/127) of the DBT double reading detected cancers could be detected\u201426% (25\/95) more than DM screening (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.001)\u2014while increasing recall rates by 53%. AI alone with DBT has a sensitivity similar to DM double reading (\n                      <jats:italic>p<\/jats:italic>\n                      = 0.689).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>AI can open up possibilities for implementing DBT screening and detecting more cancers with the total reading workload unchanged. Considering the potential legal and psychological implications, replacing the second reader with AI would probably be most the feasible approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Breast cancer screening with digital breast tomosynthesis and artificial intelligence can detect more cancers than mammography screening without increasing screen-reading workload.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Artificial intelligence can either exclude low-risk cases from double reading or replace the second reader.<\/jats:italic>\n                    <\/jats:p>\n                    <jats:p>\n                      \u2022\n                      <jats:italic>Retrospective study based on paired mammography and digital breast tomosynthesis screening data.<\/jats:italic>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-022-09316-y","type":"journal-article","created":{"date-parts":[[2022,12,11]],"date-time":"2022-12-11T00:02:20Z","timestamp":1670716940000},"page":"3754-3765","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["Breast cancer screening with digital breast tomosynthesis: comparison of different reading strategies implementing artificial intelligence"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4330-5387","authenticated-orcid":false,"given":"Victor","family":"Dahlblom","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Magnus","family":"Dustler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anders","family":"Tingberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia","family":"Zackrisson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,11]]},"reference":[{"key":"9316_CR1","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.1016\/S1470-2045(18)30521-7","volume":"19","author":"S Zackrisson","year":"2018","unstructured":"Zackrisson S, L\u00e5ng K, Rosso A et al (2018) One-view breast tomosynthesis versus two-view mammography in the Malm\u00f6 Breast Tomosynthesis Screening Trial (MBTST): a prospective, population-based, diagnostic accuracy study. 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MD and SZ hold a patent related to breast cancer screening (US 9 833 203). AT has received a research grant from Siemens Healthineers. SZ has received funding for travelling and speaker\u2019s fees from Siemens Healthineers.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was obtained from all subjects in this study at inclusion in the MBTST, also covering this specific study objective according to the Institutional Ethical Review Board approval.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval for the MBTST by the Local Ethics Committee at Lund University (Official records number: 2009\/770) also covers the present study.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"\u2022 retrospective\u2022 experimental\u2022 performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}