Multimodal Deep Learning for Differentiating Bacterial and Fungal Keratitis Using Prospective Representative Data.

ABSTRACT:

Objective

This study develops and evaluates multimodal machine learning models for differentiating bacterial and fungal keratitis using a prospective representative dataset from South India.

Design

Machine learning classifier training and validation study.

Participants

Five hundred ninety-nine subjects diagnosed with acute infectious keratitis at Aravind Eye Hospital in Madurai, India.

Methods

We developed and compared 3 prediction models to distinguish bacterial and fungal keratitis using a prospective, consecutively-collected, representative dataset gathered over a full calendar year (the MADURAI dataset). These models included a clinical data model, a computer vision model using the EfficientNet architecture, and a multimodal model combining both imaging and

SUBMITTER: Prajna NV 

PROVIDER: S-EPMC11758206 | biostudies-literature | 2025 Mar-Apr

REPOSITORIES: biostudies-literature