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

Abstract

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. 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). 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 ± 5.47µm to 100.8 ± 5.27µm, P = 0.0007) and PR + RPE thicknesses (60.9 ± 2.6µm to 66.2 ± 1.8µm, 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. Increased ONL and PR-RPE thickness associate with better outcomes, while HF presence indicates poorer results, with initial BCVA remaining a primary visual predictor. The study underscores the role of novel biomarkers like HF in understanding visual function in macula-off RRD.