Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms

Objectives To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists.Materials and methods All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AI(sens)) and specificity (AI(spec)) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR).Results The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AI(sens) had lower specificity (97.5% vs 97.7%; p < 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p < 0.0001) than first readers. AI(spec) was comparable to first readers in terms of all accuracy measures. Both AI(sens) and AI(spec) detected significantly fewer screen-detected cancers (1166 (AI(sens)), 1156 (AI(spec)) vs 1252; p < 0.0001) but found more interval cancers compared to first readers (126 (AI(sens)), 117 (AI(spec)) vs 39; p < 0.0001) with varying types of cancers detected across multiple subgroups.Conclusion Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers.