Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

ABSTRACT: A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries a

SUBMITTER: Christopher M 

PROVIDER: S-EPMC10886033 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature