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OBJECTIVES: To compare the location of AI markings on screening mammograms with cancer location on diagnostic mammograms, and to classify interval cancers with high AI score as false negative, minimal sign, or true negative. METHODS: In a retrospective study from 2022, we compared the performance of an AI system with independent double reading according to cancer detection. We found 93% (880/949) of the screen-detected cancers, and 40% (122/305) of the interval cancers to have the highest AI risk score (AI score of 10). In this study, four breast radiologists reviewed mammograms from 126 randomly selected screen-detected cancers and all 120 interval cancers with an AI score of 10. The location of the AI marking was stated as correct/not correct in craniocaudal and mediolateral oblique view. Interval cancers with an AI score of 10 were classified as false negative, minimal sign significant/non-specific, or true negative. RESULTS: All screen-detected cancers and 78% (93/120) of the interval cancers with an AI score of 10 were correctly located by the AI system. The AI markings matched in both views for 79% (100/126) of the screen-detected cancers and 22% (26/120) of the interval cancers. For interval cancers with an AI score of 10, 11% (13/120) were correctly located and classified as false negative, 10% (12/120) as minimal sign significant, 26% (31/120) as minimal sign non-specific, and 31% (37/120) as true negative. CONCLUSION: AI markings corresponded to cancer location for all screen-detected cancers and 78% of the interval cancers with high AI score, indicating a potential for reducing the number of interval cancers. However, it is uncertain whether interval cancers with subtle findings in only one view are actionable for recall in a true screening setting. CLINICAL RELEVANCE STATEMENT: In this study, AI markings corresponded to the location of the cancer in a high percentage of cases, indicating that the AI system accurately identifies the cancer location in mammograms with a high AI score. KEY POINTS: ⢠All screen-detected and 78% of the interval cancers with high AI risk score (AI score of 10) had AI markings in one or two views corresponding to the location of the cancer on diagnostic images. ⢠Among all 120 interval cancers with an AI score of 10, 21% (25/120) were classified as a false negative or minimal sign significant and had AI markings matching the cancer location, suggesting they may be visible on prior screening. ⢠Most of the correctly located interval cancers matched only in one view, and the majority were classified as either true negative or minimal sign non-specific, indicating low potential for being detected earlier in a real screening setting.
RESUMO
PURPOSE: To investigate attitudes and perspectives on the use of artificial intelligence (AI) in the assessment of screening mammograms among women invited to BreastScreen Norway. METHOD: An anonymous survey was sent to all women invited to BreastScreen Norway during the study period, October 10, 2022, to December 25, 2022 (n = 84,543). Questions were answered on a 10-point Likert scale and as multiple-choice, addressing knowledge of AI, willingness to participate in AI studies, information needs, confidence in AI results and AI assisted reading strategies, and thoughts on concerns and benefits of AI in mammography screening. Analyses were performed using χ2 and logistic regression tests. RESULTS: General knowledge of AI was reported as extensive by 11.0% of the 8,355 respondents. Respondents were willing to participate in studies using AI either for decision support (64.0%) or triaging (54.9%). Being informed about use of AI-assisted image assessment was considered important, and a reading strategy of AI in combination with one radiologist preferred. Having extensive knowledge of AI was associated with willingness to participate in AI studies (decision support; odds ratio [OR]: 5.1, 95% confidence interval [CI]: 4.1-6.4, and triaging; OR: 3.4, 95% CI: 2.8-4.0) and trust in AI's independent assessment (OR: 6.8, 95% CI: 5.7, 8.3). CONCLUSIONS: Women invited to BreastScreen Norway had a positive attitude towards the use of AI in image assessment, given that human readers are still involved. Targeted information and increased public knowledge of AI could help achieve high participation in AI studies and successful implementation of AI in mammography screening.