Work

Beyond tessellation: AI-derived choroidal vessel density on multi-channel cSLO as a surrogate biomarker for OCT choroidal parameters

Year: 2026

Type: article

Abstract: Purpose: To develop an AI-based method for quantifying choroidal vessel density (CVD) from ultra-widefield confocal scanning laser ophthalmoscopy (UWF-cSLO) and to validate this metric against swept-source optical coherence tomography (SS-OCT) choroidal parameters. Methods: In this retrospective cross-sectional study, 633 eyes from 354 participants underwent simultaneous UWF-cSLO and SS-OCT imaging on an integrated platform, ensuring precise spatial co-registration. A TransUNet deep-learning model segmented choroidal vessels from red-channel (R; 665 nm) and infrared-channel (IR; 785 nm) images, yielding channel-specific metrics (R-CVD and IR-CVD) and a combined metric (IR + R CVD). Eight SS-OCT parameters-including choroidal thickness (CT), choroidal vascularity index (CVI), luminal area, stromal area (SA), total choroidal area (TCA), and vessel volume index (VVI)-served as reference measures. Associations were evaluated using Spearman correlations with participant-level clustered bootstrap confidence intervals, multivariable generalised estimating equations (GEE) adjusted for age and sex, and receiver operating characteristic (ROC) analysis. Results: = -0.29). These channel-dependent patterns persisted after adjustment for age and sex in multivariable GEE models. On ROC analysis, R-CVD discriminated thin from thick choroids with an area under the curve (AUC) of 0.789 (sensitivity 72.5%; specificity 74.8%), whereas IR-CVD alone showed near-chance performance (AUC 0.517). Conclusions: AI-derived CVD from UWF-cSLO correlates strongly with SS-OCT choroidal parameters in a wavelength-dependent manner and may serve as an accessible surrogate for choroidal structural assessment in settings without OCT.

Source: Frontiers in Medicine

DOI: 10.3389/fmed.2026.1884151