Artificial Intelligence-Enhanced OCT Biomarkers Analysis in Macula-off Rhegmatogenous Retinal Detachment Patients.

Purpose To identify optical coherence tomography (OCT) biomarkers for macula-off rhegmatogenous retinal detachment (RRD) with artificial intelligence (AI) and to correlate these biomarkers with functional outcomes. Methods Patients with macula-off RRD treated with single vitrectomy and gas tamponade were included. OCT volumes, taken at 4 to 6 weeks and 1 year postoperative, were uploaded on an AI-derived platform (Discovery OCT Biomarker Detector; RetinAI AG, Bern, Switzerland), measuring different retinal layer thicknesses, including outer nuclear layer (ONL), photoreceptor and retinal pigmented epithelium (PR + RPE), intraretinal fluid (IRF), subretinal fluid, and biomarker probability detection, including hyperreflective foci (HF). A random forest model assessed the predictive factors for final best-corrected visual acuity (BCVA). Results Fifty-nine patients (42 male, 17 female) were enrolled. Baseline BCVA was 0.5 logarithmic minimum angle of resolution (logMAR) ± 0.1, significantly improving to 0.3 ± 0.1 logMAR at the final visit (P < 0.001). Average thickness analysis indicated a significant increase after the last follow-up visit for ONL (from 95.16 ± 5.47 µm to 100.8 ± 5.27 µm, P = 0.0007) and PR + RPE thicknesses (60.9 ± 2.6 µm to 66.2 ± 1.8 µm, P = 0.0001). Average occurrence rate of HF was 0.12 ± 0.06 at initial visit and 0.08 ± 0.05 at last follow-up visit (P = 0.0093). Random forest model revealed baseline BCVA as the most critical predictor for final BCVA, followed by ONL thickness, HF, and IRF presence at the initial visit. Conclusions Increased ONL and PR-RPE thickness associate with better outcomes, while HF presence indicates poorer results, with initial BCVA remaining a primary visual predictor. Translational Relevance The study underscores the role of novel biomarkers like HF in understanding visual function in macula-off RRD.