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

Table 3 Diagnostic efficacy of artificial intelligence in screening diabetic retinopathy in natural population and diabetes population based on single orientation fundus photography of each subject

Different DR classifications	Natural population			People with diabetes
	AUC (95%CI)	Sensitivity (95%CI)	Specificity (95%CI)	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%)	0.901 (0.884-0.916)	98.1% (89.7%-100.0%)	82.1% (79.9%-84.2%)
Different degrees of DR	0.881 (0.874-0.888)	83.7% (79.4%-87.4%)	92.5% (91.9%-93.1%)	0.903 (0.886-0.918)	91.6% (86.3%-95.3%)	89.0% (87.0%-90.7%)
Severe DR	0.948 (0.943-0.952)	100.0% (39.8%-100.0%)	89.6% (88.9%-90.2%)	0.896 (0.878-0.912)	100.0% (29.2%-100.0%)	79.6% (76.9%-81.3%)

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