Table 1.

Discriminative performance of image-based DL models according to 5-fold cross-validation

Model	Dataset	Accuracy [Variance, (95%CI)]	Sensitivity [Variance, (95%CI)]	Specificity [Variance, (95%CI)]	AUC [Variance, (95%CI)]
ResNet50	Training	0.999 (0.000, [0.997–1.000])	0.998 (0.000, [0.996–1.000])	1.000 (0.000, [1.000–1.000])	1.000 (0.000, [1.000–1.000])
ResNet50	Validation	0.813 (0.004, [0.734–0.892])	0.829 (0.019, [0.769–0.890])	0.790 (0.007, [0.707–0.872])	0.876 (0.004, [0.813–0.936])
ConvNeXt-B	Training	1.000 (0.000, [1.000–1.000])	1.000 (0.000, [1.000–1.000])	1.000 (0.000, [1.000–1.000])	1.000 (0.000, [1.000–1.000])
ConvNeXt-B	Validation	0.834 (0.008, [0.762–0.905])	0.823 (0.010, [0.758–0.888])	0.845 (0.010, [0.778–0.911])	0.897 (0.005, [0.843–0.949])
ViT_B/16	Training	0.999 (0.000, [0.998–1.000])	0.999 (0.000, [0.998–1.000])	1.000 (0.000, [1.000–1.000])	0.999 (0.000, [0.998–1.000])
ViT_B/16	Validation	0.650 (0.006, [0.551–0.749])	0.706 (0.013, [0.614–0.797])	0.578 (0.006, [0.476–0.680])	0.664(0.011, [0.567–0.761])

Abbreviation: DL deep learning, 95%CI 95% confidence interval, AUC area under the curve