Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection
Abstract
Summary Background Artificial intelligence (AI) has been proposed to reduce false-positive screens, increase cancer detection rates (CDRs), and address resourcing challenges faced by breast screening programs. We compared the accuracy of AI versus radiologists in real-world population breast cancer screening, and estimated potential impacts on CDR, recall and workload for simulated AI-radiologist reading. Methods External validation of a commercially-available AI algorithm in a retrospective cohort of 108,970 consecutive mammograms from a population-based screening program, with ascertained outcomes (including interval cancers by registry linkage). Area under the ROC curve (AUC), sensitivity and specificity for AI were compared with radiologists who interpreted the screens in practice. CDR and recall were estimated for simulated AI-radiologist reading (with arbitration) and compared with program metrics. Findings The AUC for AI was 0.83 compared with 0.93 for radiologists. At a prospective threshold, sensitivity for AI (0.67; 95% CI: 0.64–0.70) was comparable to radiologists (0.68; 95% CI: 0.66–0.71) with lower specificity (0.81 [95% CI: 0.81–0.81] versus 0.97 [95% CI: 0.97–0.97]). Recall rate for AI-radiologist reading (3.14%) was significantly lower than for the BSWA program (3.38%) (−0.25%; 95% CI: −0.31 to −0.18; P < 0.001). CDR was also lower (6.37 versus 6.97 per 1000) (−0.61; 95% CI: −0.77 to −0.44; P < 0.001); however, AI detected interval cancers that were not found…
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