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. 2023 Sep 20;48(1):183–191. doi: 10.1007/s00264-023-05987-4

Table 1.

The diagnostic effect of the SLAP-Net model and three radiologists with different seniority levels on datasets 1 and 2 and a comparison between the model and manual diagnosis

Dataset Group AUC Accuracy Sensitivity Specificity PPV NPV χ2 p value*
SLAP-Net Dataset 1** Class 0 0.98 0.96 0.94 1.00 1.00 0.91 / /
Class 1 0.98 0.96 1.00 0.94 0.91 1.00 / /
Dataset 2** Class 0 0.92 0.85 0.90 0.76 0.86 0.84 / /
Class 1 0.92 0.85 0.76 0.90 0.84 0.86 / /
Radiologist 1 (15 years of experience) Dataset 1 Class 0 / 0.85 0.91 0.76 0.86 0.84 3.68 0.055
Class 1 / 0.85 0.76 0.91 0.84 0.86
Dataset 2 Class 0 / 0.85 0.86 0.84 0.91 0.77 0.53 0.468
Class 1 / 0.85 0.84 0.86 0.77 0.91
Radiologist 2 (10 years of experience) Dataset 1 Class 0 / 0.83 0.81 0.85 0.90 0.74 5.04 0.025
Class 1 / 0.83 0.85 0.81 0.74 0.90
Dataset 2 Class 0 / 0.86 0.89 0.82 0.90 0.80 1.13 0.289
Class 1 / 0.86 0.82 0.89 0.80 0.90
Radiologist 3 (7 years of experience) Dataset 1 Class 0 / 0.81 0.78 0.85 0.89 0.71 6.50 0.011
Class 1 / 0.81 0.85 0.78 0.71 0.89
Dataset 2 Class 0 / 0.81 0.86 0.73 0.85 0.74 0.47 0.495
Class 1 / 0.81 0.73 0.86 0.74 0.85

AUC area under the receiver operating characteristic curve, PPV positive predictive value, NPV negative predictive value, χ2 the chi-square value calculated by McNemar’s test

*The results of comparing different radiologists with SLAP-Net on dataset 1 and dataset 2; McNemar’s test was used for the statistical analysis

**Dataset 1 contains 52 test patients, and dataset 2 is an independent test set that contains 122 patients