Screening and evaluation of diabetic retinopathy via a deep learning network model: A prospective study

Table 4 Diagnostic efficacy of artificial intelligence single directional fundus photography and image reading screening for diabetic retinopathy in different diabetic retinopathy classification populations

Different DR classifications	AUC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)
RDR	0.941 (0.936-0.946)	98.2% (90.1%-100.0%)	90.1% (89.4%-90.7%)
RDR (non-hypertensive population)	0.965 (0.960-0.970)	100.0% (79.4%-100.0%)	93.1% (92.3%-93.8%)
RDR (hypertensive population)	0.920 (0.911-0.928)	97.4% (86.2%-99.9%)	86.6% (85.5%-87.6%)
RDR (normal vision population)	0.962 (0.952-0.969)	100.0% (78.2%-100.0%)	92.3% (91.1%-93.4%)
RDR (low vision population)	0.923 (0.912-0.933)	93.8% (69.8%-99.8%)	90.8% (89.7%-91.9%)
RDR (low vision group)	0.948 (0.908-0.975)	100.0% (29.2%-100.0%)	89.7% (84.5%-93.6%)
RDR (non-low vision group)	0.939 (0.932-0.946)	96.3% (81.0%-99.9%)	91.5% (90.6%-92.3%)

AUC: Area under the curve; RDR: Referable diabetic retinopathy.