Extended Data Fig. 3: Localization (mLROC) analysis.
From: International evaluation of an AI system for breast cancer screening
Similar to Extended Data Fig. 2, but true positives require localization of a malignancy in any of the four mammogram views (see Methods section ‘Localization analysis’). Here, the cancer interval was 12 months (n = 53 positives of 405 cases; see Extended Data Table 3). The dotted line indicates a false-positive rate of 10%, which was used as the right-hand boundary for the pAUC calculation. The mean reader pAUC was 0.029 (s.d. 0.005), whereas that of the AI system was 0.048 (95% CI 0.035, 0.061). The AI system exceeded human performance by a significant margin (ΔpAUC = +0.0192, 95% CI 0.0086, 0.0298; P = 0.0004 by two-sided ORH method).
