Accuracy of the EyeArt artificial intelligence system in detecting referable diabetic retinopathy in patients with diabetes in Hong Kong
Keywords:Artificial intelligence; , Diabetic retinopathy, Macular edema
Objective: This study evaluated the real-world performance in an Asian population with diabetes mellitus (DM) of the EyeArt system, which is the only United States Food and Drug Administration–approved system available in Hong Kong.
Methods: Fundus photographs taken from December 2020 to June 2021 during a diabetic retinopathy (DR) screening program in patients with DM who attended Pamela Youde Nethersole Eastern Hospital in Hong Kong were retrieved. Two vitreoretinal-trained ophthalmologists independently graded the fundus photographs for DR and concomitant pathologies. Discrepancies were resolved through discussion with a senior vitreoretinal ophthalmologist for a final decision. Fundus photographs (in jpg format) were then uploaded to the EyeArt AI system. The EyeArt system’s sensitivity and specificity on detecting (1) referable DR (moderate or severe non-proliferative DR, proliferative DR, or diabetic macular edema [DME]) and (2) any DR or DME were calculated based on the ophthalmologists’ diagnoses (the gold standard).
Results: 23 men and 79 women (93 Chinese, 3 Pakistanis, 2 Thais, and 4 Indonesians) aged 25 to 77 years with type1 (n=8) or type 2 (n=94) DM were included for analysis. The mean duration of DM was 4.9 years; 56 (54.9%)
patients were newly diagnosed with DM. 33 (32.4%) patients were taking ≥1 (up to 4) oral hypoglycemic agents; none were taking ≥5. 16 (15.7%) patients required both oral hypoglycemic agents and insulin for DM control; 31 (30.4%) patients injected insulin every day. As diagnosed by the ophthalmologists, 41 (20.1%) of 204 eyes had DR and/or DME, whereas 26 (25.5%) of 102 patients had referable DR. There was no discrepancy between the two ophthalmologists’ gradings. On detecting referable DR, the EyeArt system had 96.2% sensitivity, 94.7% specificity, 86.2% positive predictive value, 98.6% negative predictive value, and 95.1% accuracy. On detecting any DR or DME, the EyeArt system had 92.7% sensitivity, 93.7% specificity, 79.2% positive predictive value, 98.0% negative predictive value, and 93.5% accuracy.
Conclusion: The EyeArt system is a reliable AI tool for DR screening in patients with DM in Hong Kong. Its usage in primary care and public healthcare programs should be considered.
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