Prediction of chronic central serous chorioretinopathy through combined manual annotation and AI-assisted volume measurement of flat irregular pigment epithelium.

Abstract

Introduction: The aim of this study is to investigate the role of an artificial intelligence (AI)-developed OCT program to predict the clinical course of central serous chorioretinopathy (CSC ) based on baseline pigment epithelium detachment (PED) features.

Methods: Single-center, observational study with a retrospective design. Treatment-naïve patients with acute CSC and chronic CSC were recruited and OCTs were analyzed by an AI-developed platform (Discovery OCT Fluid and Biomarker Detector, RetinAI AG, Switzerland), providing automatic detection and volumetric quantification of PEDs. Flat irregular PED presence was annotated manually and afterwards measured by the AI program automatically.

Results: 115 eyes of 101 patients with CSC were included, of which 70 were diagnosed with chronic CSC and 45 with acute CSC. It was found that patients with baseline presence of foveal flat PEDs and multiple flat foveal and extrafoveal PEDs had a higher chance of developing chronic form. AI-based volumetric analysis revealed no significant differences between the groups.

Conclusions: While more evidence is needed to confirm the effectiveness of AI-based PED quantitative analysis, this study highlights the significance of identifying flat irregular PEDs at the earliest stage possible in patients with CSC, to optimize patient management and long-term visual outcomes.