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dc.contributor.authorLarsen, Marthe
dc.contributor.authorOlstad, Camilla Flåt
dc.contributor.authorLee, Christoph I.
dc.contributor.authorHovda, Tone
dc.contributor.authorHoff, Solveig Kristin Roth
dc.contributor.authorMartiniussen, Marit Almenning
dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorLund-Hanssen, Håkon
dc.contributor.authorSolli, Helene
dc.contributor.authorSilberhorn, Marko
dc.contributor.authorSulheim, Åse Ø.
dc.contributor.authorAuensen, Steinar Gøytil
dc.contributor.authorNygård, Jan Franz
dc.contributor.authorHofvind, Solveig Sand-Hanssen
dc.date.accessioned2024-10-07T09:23:56Z
dc.date.available2024-10-07T09:23:56Z
dc.date.issued2024-04-10
dc.description.abstractA commercially available artificial intelligence system showed high performance in detecting breast cancers within 2 years of screening mammography and may help triage low-risk mammograms to reduce radiologist workload.<p> <p>Purpose - To explore the stand-alone breast cancer detection performance, at different risk score thresholds, of a commercially available artificial intelligence (AI) system.<p> <p>Materials and Methods - This retrospective study included information from 661 695 digital mammographic examinations performed among 242 629 female individuals screened as a part of BreastScreen Norway, 2004–2018. The study sample included 3807 screen-detected cancers and 1110 interval breast cancers. A continuous examination-level risk score by the AI system was used to measure performance as the area under the receiver operating characteristic curve (AUC) with 95% CIs and cancer detection at different AI risk score thresholds.<p> <p>Results - The AUC of the AI system was 0.93 (95% CI: 0.92, 0.93) for screen-detected cancers and interval breast cancers combined and 0.97 (95% CI: 0.97, 0.97) for screen-detected cancers. In a setting where 10% of the examinations with the highest AI risk scores were defined as positive and 90% with the lowest scores as negative, 92.0% (3502 of 3807) of the screen-detected cancers and 44.6% (495 of 1110) of the interval breast cancers were identified with AI. In this scenario, 68.5% (10 987 of 16 040) of false-positive screening results (negative recall assessment) were considered negative by AI. When 50% was used as the cutoff, 99.3% (3781 of 3807) of the screen-detected cancers and 85.2% (946 of 1110) of the interval breast cancers were identified as positive by AI, whereas 17.0% (2725 of 16 040) of the false-positive results were considered negative.<p> <p>Conclusion - The AI system showed high performance in detecting breast cancers within 2 years of screening mammography and a potential for use to triage low-risk mammograms to reduce radiologist workload.en_US
dc.identifier.citationLarsen, Olstad, Lee, Hovda, Hoff, Martiniussen, Mikalsen, Lund-Hanssen H, Solli H, Silberhorn, Sulheim, Auensen, Nygård, Hofvind. Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway. Radiology: Artificial Intelligence (RAI). 2024;6(3)en_US
dc.identifier.cristinIDFRIDAID 2287630
dc.identifier.doi10.1148/ryai.230375
dc.identifier.issn2638-6100
dc.identifier.urihttps://hdl.handle.net/10037/35078
dc.language.isoengen_US
dc.publisherRadiological Society of North Americaen_US
dc.relation.journalRadiology: Artificial Intelligence (RAI)
dc.relation.urihttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11140504/
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2024 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0en_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)en_US
dc.titlePerformance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norwayen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Med mindre det står noe annet, er denne innførselens lisens beskrevet som Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)