ObjectivesTo evaluate and preliminarily analyze the application value and efficacy of artificial intelligence optical coherence tomography (AI-OCT) technology in the early screening of retinal diseases among the elderly, hypertension, hyperglycemia, high myopia and hyperlipidemia (referred to as "Five-High") population. Methods A diagnostic trial was conducted. A total of 3 834 patients (7 668 eyes) with "Five-High" risk factors who visited the outpatient clinics of Shenyang Fourth People’s Hospital from July to December 2024 were included. Optical coherence tomography imaging of the macular and peripheral retina was performed using the Bigway AI-OCT image analysis system (wide-field three-dimensional scanning mode). The deep learning-based system automatically identified and labeled eight types of high-risk retinal lesions: subretinal fluid (SRF), intraretinal fluid (IRF), epiretinal membrane (ERM), choroidal neovascularization (CNV), hyper-reflective foci (HRF), retinal pigment epithelium detachment, retinal hemorrhage, and macular hole (MH). The positive rate of AI-OCT screening and the distribution of high-risk lesions were analyzed. Consistency between AI-OCT screening results and ophthalmologist review was assessed using Cohen’s Kappa test. Logistic regression was used to identify independent predictors of positive AI-OCT screening. Referral and treatment rates were also analyzed. ResultsAmong 3 834 cases involving 7 668 eyes, 803 cases (1 606 eyes) were positive in AI-OCT screening, with a positive rate of 20.9% (803/3 834), including 266 high-risk and 537 non-high-risk patients, respectively. The positive screening rates of patients with "Five Highs" were as follows: hyperlipidemia 25.2% (185/735), advanced age 24.9% (746/1 998), hyperglycemia 24.8% (345/1 392), hypertension 23.8% (228/956), and high myopia 19.0% (40/210). Among 1 606 positive eyes, 1 355 high-risk lesions were identified by consensus. Among them, ERM had the largest number of identifications (780, 57.6%), followed by HRF (255, 18.8%), and MH had the smallest number of identifications (7, 0.5%). Physicians randomly reexamined 1 352 cases and 2 704 eyes. The number of positive and negative eyes diagnosed was 753 and 1 952 respectively. The number of positive and negative eyes screened by AI-OCT was 828 and 1 876 respectively. There was an excellent consistency between AI-OCT screening and physician diagnosis (Kappa=0.866, P=0.011). Multivariate logistic regression analysis showed that age [odds ratio (OR) =1.071, P＜0.001], high myopia (OR=1.921, P=0.001), and hyperglycemia (OR=1.287, P=0.005) were independent predictors of positive AI-OCT screening. Among 1 355 high-risk lesions, a total of 703 were referred (referral rate 51.9%). The three lesions with the highest referral rates were SRF (71.1%, 27/38), IRF (69.2%, 54/78), and CNV (61.5%, 24/39), respectively. Among the 803 cases with positive AI-OCT screening, 385 cases (47.9%) actually received referral suggestions, 259 cases (32.3%) were eventually diagnosed, and 109 cases (13.6%) received treatment. Compared with low-risk patients, the referral rate and diagnosis rate of high-risk patients were significantly higher (χ2=6.87, 4.48; P＜0.05), but there was no statistically significant difference in the final treatment acceptance rate between groups (χ2=1.15, P=0.280). ConclusionsThe established AI-OCT based screening model for fundus diseases in the “Five-High” population effectively improves the detection rate of early-stage lesions and promotes a shift from universal to precision screening. Patients with positive screening results have obvious referral and treatment obstacles, which requires clinical attention.