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

Table 1 Comparison of baseline data between non-diabetic retinopathy and diabetic retinopathy groups of study subjects

Clinical indicators	Non-DR group (n = 255)	DR group (n = 219)	t/χ2 value	P value
Age (years, mean ± SD)	49.88 ± 12.05	54.21 ± 9.02	-6.62	< 0.01
Male [n (%)]	86 (33.73)	87 (39.73)	4.94	0.026
SBP (mmHg, mean ± SD)	132 ± 21	141 ± 23	-7.96	< 0.01
DBP (mmHg, mean ± SD)	81 ± 12	85 ± 13	-4.99	< 0.01
FPG (mmol/L, mean ± SD)	5.77 ± 1.35	7.70 ± 3.54	-23.56	< 0.01
2hpg (mmol/L, mean ± SD)	7.48 ± 3.43	11.52 ± 6.83	-20.30	< 0.01
HbA1c (%, mean ± SD)	5.57 ± 0.88	6.70 ± 2.02	-21.87	< 0.01
Hypertension [n (%)]	114 (44.71)	142 (64.84)	50.78	< 0.01

DR: Diabetic retinopathy; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; FPG: Fasting plasma glucose; 2hpg: 2-hour-post glucose detection; HbA1c: Hemoglobin A1c.

Table 2 Diagnostic efficacy of artificial intelligence in screening diabetic retinopathy based on single direction fundus photography for each eye in natural population and diabetes population

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	0.936 (0.932-0.940)	93.0% (85.4%-97.4%)	94.2% (93.8%-94.6%)	0.911 (0.900-0.922)	94.0% (86.5%-98.0%)	88.3% (86.9%-89.5%)
Different degrees of DR	0.875 (0.870-0.880)	79.3% (75.3%-82.9%)	95.8% (95.4%-96.1%)	0.891 (0.878-0.903)	85.0% (79.9%-89.2%)	93.2% (92.1%-94.2%)
Severe DR	0.898 (0.893-0.902)	85.7% (42.1%-99.6%)	93.8% (93.4%-94.2%)	0.929 (0.918-0.938)	100.0% (47.8%-100.0%)	85.8% (84.3%-87.1%)

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

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.

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.