Objectives To compare results of selected performance measures in mammographic screening for an artificial intelligence (AI) system versus independent double reading by radiologists. 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 stratified by mammographic density. 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. 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. Key Points • The AI system scored 93% of the screen-detected cancers and 40% of the interval cancers with AI score 10. • The AI system scored all screen-detected cancers and almost 50% of interval cancers among women with the highest breast density with AI score 10. • About 40% of the screen-detected cancers had an AI score of 10 on the prior mammograms, indicating a potential for earlier detection by using AI in screen-reading.

Stavanger universitetssjukehus Kari Wiig Stangeland er lege i spesialisering i nyresykdommer. Forfa eren har fylt ut ICMJE-skjemaet og oppgir ingen interessekonflikter. Nevrokirurgisk avdeling Stavanger universitetssjukehus Roald Baardsen er spesialist i nevrokirurgi og avdelingsoverlege. Forfa eren har fylt ut ICMJE-skjemaet og oppgir ingen interessekonflikter. Nevrokirurgisk avdeling Stavanger universitetssjukehus og Det helsevitenskapelige fakultet Universitetet i Stavanger Clemens Weber er spesialist i nevrokirurgi, overlege og postdok. Forfa eren har fylt ut ICMJE-skjemaet og oppgir ingen interessekonflikter. Infeksjonsmedisinsk seksjon Stavanger universitetssjukehus Børge Førland Gjøse er spesialist i indremedisin og infeksjonssykdommer og seksjonsoverlege. Forfa eren har fylt ut ICMJE-skjemaet og oppgir ingen interessekonflikter. Øre-nese-hals-avdelingen Stavanger universitetssjukehus Lynn Elisabeth Biserød er spesialist i øre-nese-halssykdommer og seksjonsoverlege. Forfa eren har fylt ut ICMJE-skjemaet og oppgir ingen interessekonflikter. Stavanger universitetssjukehus og Det helsevitenskapelige fakultet Universitetet i Stavanger En mann i 50-årene med smerter i hode, nakke og øregang | Tidsskrift for Den norske legeforening