Application of Artificial Intelligence-based Dual-modality Analysis Combining Fundus Photography and Optical Coherence Tomography in Diabetic Retinopathy Screening in a Community Hospital

Background: To assess the feasibility and application value of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital. Methods: Fundus photos and OCT images were taken for 600 diabetic patients in a community hospital. Ophthalmologists and AI identified these fundus photos according to international DR standards. OCT images were used to detect concomitant macular oedema (ME). The criteria for referral were DR grades 2-4 and/or the presence of ME. The sensitivity and specificity of AI grading were evaluated. The number of referable DR cases confirmed by ophthalmologists and AI was calculated. Results: DR was detected in 81 (13.5%) participants by ophthalmologists and in 94 (15.6%) by AI, and 45 (7.5%) and 53 (8.8%) participants were diagnosed with referable DR by ophthalmologists and by AI, respectively. The sensitivity, specificity and area under the curve (AUC) of AI for detecting DR were 91.67%, 96.92% and 0.944, respectively. For detecting referable DR, the sensitivity, specificity and AUC of AI were 97.78%, 98.38% and 0.981, respectively. ME was detected from OCT images in 49 (8.2%) participants by ophthalmologists and in 57 (9.5%) by AI, and the sensitivity, specificity and AUC of AI were 91.30%, 97.46% and 0.944, respectively. When combining fundus photos and OCT images, the number of referrals identified by ophthalmologists increased from 45 to 75 and from 53 to 85 by AI. Conclusion: AI-based DR screening has high sensitivity and specificity and may feasibly improve the referral rate of community DR.