Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model.

ABSTRACT:

Objective

To develop an explainable multimodal large language model (MM-LLM) that (1) screens optic nerve head (ONH) OCT circle scans for quality and (2) generates structured clinical reports that include glaucoma diagnosis and sector-wise retinal nerve fiber layer (RNFL) thinning assessments.

Design

Retrospective cohort study using longitudinal data from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES).

Participants

43,849 Spectralis ONH OCT circle scans from 1,310 subjects, including 1,331 glaucomatous and 867 healthy eyes.

Methods

A MM-LLM (Llama 3.2 Vision-Instruct model) was fine-tuned to generate clinical descriptions of OCT imaging data. Training data included paired OCT images and automat

SUBMITTER: Jalili J 

PROVIDER: S-EPMC12622204 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature