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dc.contributor.authorKoch, Henrik Wethe
dc.contributor.authorLarsen, Marthe
dc.contributor.authorBartsch, Hauke
dc.contributor.authorKurz, Kathinka Dæhli
dc.contributor.authorHofvind, Solveig Sand-Hanssen
dc.date.accessioned2023-08-31T07:59:10Z
dc.date.available2023-08-31T07:59:10Z
dc.date.issued2023-03-14
dc.description.abstractObjectives To compare results of selected performance measures in mammographic screening for an artifcial intelligence (AI) system versus independent double reading by radiologists.<p> <p>Methods In this retrospective study, we analyzed data from 949 screen-detected breast cancers, 305 interval cancers, and 13,646 negative examinations performed in BreastScreen Norway during the period from 2010 to 2018. An AI system scored the examinations from 1 to 10, based on the risk of malignancy. Results from the AI system were compared to screening results after independent double reading. AI score 10 was set as the threshold. The results were stratifed by mammographic density. <p>Results A total of 92.7% of the screen-detected and 40.0% of the interval cancers had an AI score of 10. Among women with a negative screening outcome, 9.1% had an AI score of 10. For women with the highest breast density, the AI system scored 100% of the screen-detected cancers and 48.6% of the interval cancers with an AI score of 10, which resulted in a sensitivity of 80.9% for women with the highest breast density for the AI system, compared to 62.8% for independent double reading. For women with screen-detected cancers who had prior mammograms available, 41.9% had an AI score of 10 at the prior screening round. <p>Conclusions The high proportion of cancers with an AI score of 10 indicates a promising performance of the AI system, particularly for women with dense breasts. Results on prior mammograms with AI score 10 illustrate the potential for earlier detection of breast cancers by using AI in screen-reading.en_US
dc.identifier.citationKoch, Larsen, Bartsch, Kurz, Hofvind. Artificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer cases. European Radiology. 2023en_US
dc.identifier.cristinIDFRIDAID 2142130
dc.identifier.doi10.1007/s00330-023-09461-y
dc.identifier.issn0938-7994
dc.identifier.issn1432-1084
dc.identifier.urihttps://hdl.handle.net/10037/30568
dc.language.isoengen_US
dc.publisherSpringer Natureen_US
dc.relation.journalEuropean Radiology
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleArtificial intelligence in BreastScreen Norway: a retrospective analysis of a cancer-enriched sample including 1254 breast cancer casesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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