An AI-Based OCT System to Detect Diabetic Macular Edema

Importance: Screening for diabetic retinopathy using fundus photographs is the global standard of care but results in high false-positive referrals to evaluate diabetic macular edema (DME), placing a substantial burden on specialist eye clinics. Integrating an AI-based optical coherence tomography (AI-OCT) system into screening pathways may reduce potentially unnecessary referrals. Objective: To evaluate the diagnostic and referral performance of an AI-OCT system for DME detection within a diabetic retinopathy screening pathway in clinical settings. Design, Setting, and Participants: Stepwise evaluation conducted in Hong Kong Special Administrative Region: a prospective silent-mode validation (February 2020 to July 2023) recruiting 603 patients with diabetes at a tertiary hospital triage unit, followed by a multicenter noninferiority RCT (September 2023 to April 2025), with follow-up completed in May 2025, recruiting 276 patients with suspected DME referred from a territory-wide diabetic retinopathy screening program. Interventions: RCT participants were randomized to intervention (referral for DME evaluation based on both fundus photograph-based screening reports and AI-OCT reports [n = 137]) or control (automatic referral based solely on fundus photograph-based screening reports [n = 139]) groups. The AI-OCT system incorporated image-quality assessment, DME detection, and uncertainty flagging. Study outcomes focused on referral rates under the 2 pathways; for ethical reasons, all participants ultimately underwent specialist evaluation. Main Outcomes and Measures: The primary outcome was false-positive DME referral rate, with a prespecified noninferiority margin of 20%. The secondary outcomes included sensitivity and specificity for DME detection and DME referral. Results: In prospective silent-mode validation (mean age, 64.7 [SD, 9.4] years; 56.2% male), 86 of 1200 scans (7.2%) were identified as ungradable and 49 of 1114 gradable scans (4.4%) were classified as uncertain. The system achieved 98.8% (95% CI, 94.5%-100.0%) sensitivity and 90.7% (95% CI, 88.7%-92.4%) specificity for DME detection. In the RCT (mean age, 63.9 [SD, 10.9] years; 54.7% male), DME prevalence was similar in the intervention and control groups (30.9% vs 29.9%). The false-positive DME referral rate was 24.1% (95% CI, 14.6%-37.0%) and 69.1% (95% CI, 61.0%-76.1%), respectively (absolute difference, -45% [95% CI, -58.2% to -31.9%; P < .001 for noninferiority]; upper bound of the CI below the prespecified noninferiority margin of 20%). Sensitivity for DME referral was 100.0% (95% CI, 100.0%-100.0%) in both groups. Specificity for DME referral was 86.5% (95% CI, 79.3%-92.9%) in the intervention group and 0.0% (95% CI, 0.0%-0.0%) in the control group. No cases of DME occurred among nonreferred participants in the intervention group. Conclusions and Relevance: Compared with standard practice, incorporation of the AI-OCT system as a secondary screening tool was noninferior with respect to false-positive referral rates and was associated with a substantial reduction in potentially unnecessary DME referrals without compromising sensitivity. Trial Registration: Chinese Clinical Trial Registry: ChiCTR2300075087.