Dynamic Prediction of Treatment Failure in Ocular Tuberculosis Using Machine Learning and Explainable AI.

ABSTRACT:

Purpose

Ocular tuberculosis (OTB) poses significant challenges in treatment because of its complex diagnostic and therapeutic landscapes. Predicting treatment failure effectively is crucial for timely intervention and improving patient outcomes. We report the application of machine learning (ML) approaches to (i) allow predictions using baseline data and (ii) dynamically update predictions based on patient history and new observations.

Methods

The Collaborative Ocular Tuberculosis Study (COTS) was a multinational retrospective study encompassing data from 836 patients with tubercular uveitis across 27 international eye care centers. This study evaluated the performance of nine ML models to predict treatment failure at six, 12, and 24 months using baseline and longitudinal da

SUBMITTER: Rojas-Carabali W 

PROVIDER: S-EPMC12574733 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature