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Table 2 Performance metrics for the DL model in the test sets

From: Deep learning based on ultrasound images assists breast lesion diagnosis in China: a multicenter diagnostic study

  AUC (95% CI) Sensitivity (95% CI) Specificity (95% CI) PPV (95% CI) NPV (95% CI) ACC F1 MCC
Internal test set 0.908 (0.879–0.933) 83.23 (76.55–88.65) 83.61 (78.97–87.58) 72.83 (67.33–77.71) 90.43 (86.96–93.04) 83.48 (79.79–86.73) 0.777 0.650
External test sets 0.913 (0.881–0.939) 88.84 (83.72–92.79) 83.77 (77.76–88.70) 85.51 (81.00–89.10) 87.43 (82.48–91.13) 86.40 (82.63–89.61) 0.871 0.728
External test set A 0.908 (0.859–0.945) 88.00 (79.98–93.64) 85.57 (76.97–91.88) 86.28 (79.39–91.12) 87.37 (80.17–92.21) 86.80 (81.26–91.19) 0.871 0.736
External test set B 0.918 (0.871–0.952) 89.62 (82.19–94.71) 81.92 (72.63–89.10) 84.82 (78.34–89.62) 87.50 (79.87–92.51) 86.00 (80.41–90.49) 0.872 0.719
  1. DL deep learning, AUC area under the receiver operating characteristic curve, PPV positive predictive value, NPV negative predictive value, ACC accuracy, MCC Matthews correlation coefficient, CI confidence interval