Development and validation of a deep-learning-based diagnostic model for drug-induced liver injury using computed tomography images

Table 3 Patient-level performance of the deep learning-based diagnostic models on internal and external test cohort

Cohort	Model	AUC (95%CI)	ACC	SEN	SPE	PPV	NPV
Internal	Model 64	0.853 (0.678-1.000)	0.919	0.750	0.966	0.857	0.933
	Model 96	0.944 (0.829-1.000)	0.865	0.875	0.862	0.636	0.962
	Model 128	0.927 (0.797-1.000)	0.919	0.750	0.966	0.857	0.933
	Model 160	0.935 (0.813-1.000)	0.865	0.875	0.862	0.636	0.962
External	Model 64	0.871 (0.772-0.971)	0.854	0.821	0.900	0.920	0.783
	Model 96	0.873 (0.774-0.972)	0.854	0.821	0.900	0.920	0.783
	Model 128	0.893 (0.802-0.984)	0.854	0.821	0.900	0.920	0.783
	Model 160	0.891 (0.799-0.983)	0.875	0.857	0.900	0.923	0.818

AUC: Area under the curve; CI: Confidence interval; ACC: Accuracy; SEN: Sensitivity; SPE: Specificity; PPV: Positive predictive value; NPV: Negative predictive value.