Table 2.

Final results of deep learning networks performance

Task	Network	AUC	Accuracy	Sensitivity	Specificity	PPV	NPV
1	AlexNet	0.98	0.95 (0.94−0.96)	0.98 (0.98−0.98)	0.89 (90.88−0.90)	0.96 (0.95−0.97)	0.94 (0.93−0.95)
	ResNet18	0.98	0.95 (0.94−0.96)	0.96 (0.95−0.97)	0.91 (0.90−0.92)	0.96 (0.95−0.97)	0.91 (0.90−0.92)
	ResNet34	0.98	0.95 (0.94−0.96)	0.97 (0.96−0.98)	0.9 (0.89−0.91)	0.96 (0.95−0.97)	0.91 (0.90−0.92)
2	AlexNet	0.94	0.87 (0.86−0.88)	0.85 (0.84−0.86)	0.89 (0.88−0.90)	0.87 (0.86−0.88)	0.88 (0.87−0.89)
	ResNet18	0.88	0.80 (0.78−0.82)	0.75 (0.73−0.77)	0.85 (0.84−0.86)	0.80 (0.78−0.82)	0.80 (0.78−0.82)
	ResNet34	0.92	0.84 (0.83−0.85)	0.84 (0.83−0.85)	0.84 (0.83−0.85)	0.81 (0.79−0.83)	0.87 (0.86−0.88)

95% confidence intervals in parentheses. Best performance in bold. AUC Area under the curve, NPV Negative predictive value, PPV Positive predictive value