Multimodal Artificial Intelligence Models Predicting Glaucoma Progression Using Electronic Health Records and Retinal Nerve Fiber Layer Scans.

ABSTRACT:

Purpose

The purpose of this study was to develop models that predict which patients with glaucoma will progress to require surgery, combining structured data from electronic health records (EHRs) and retinal fiber layer optical coherence tomography (RNFL OCT) scans.

Methods

EHR data (demographics and clinical eye examinations) and RNFL OCT scans were identified for patients with glaucoma from an academic center (2008-2023). Comparing the novel TabNet deep learning architecture to a baseline XGBoost model, we trained and evaluated single modality models using either EHR or RNFL features, as well as fusion models combining both EHR and RNFL features as inputs, to predict glaucoma surgery within 12 months (binary).

Results

We had 1472 patients with glaucoma who were incl

SUBMITTER: Koornwinder A 

PROVIDER: S-EPMC11954538 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature