Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.

ABSTRACT:

Background/ aims

The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We established the normative distribution of anterior chamber depth (ACD), area (ACA) and width (ACW) and lens vault (LV), and applied percentile cut-offs to detect primary angle closure disease (PACD; ≥180° posterior trabecular meshwork occluded).

Methods

We included subjects from the Singapore Chinese Eye Study with ASOCT scans. Eyes with ocular surgery or laser procedures, and ocular trauma were excluded. A deep-learning algorithm was used to obtain Visante ASOCT (Carl Zeiss Meditec, USA) measurements. Normative distribution was established using 80% of eyes with open angles. Multivariable logistic regression was performed on 80% open a

SUBMITTER: Soh ZD 

PROVIDER: S-EPMC12013577 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature