Patient selection for corneal topographic evaluation of keratoconus: A screening approach using artificial intelligence.

ABSTRACT:

Background

Corneal topography is a clinically validated examination method for keratoconus. However, there is no clear guideline regarding patient selection for corneal topography. We developed and validated a novel artificial intelligence (AI) model to identify patients who would benefit from corneal topography based on basic ophthalmologic examinations, including a survey of visual impairment, best-corrected visual acuity (BCVA) measurement, intraocular pressure (IOP) measurement, and autokeratometry.

Methods

A total of five AI models (three individual models with fully connected neural network including the XGBoost, and the TabNet models, and two ensemble models with hard and soft voting methods) were trained and validated. We used three datasets collected from the record

SUBMITTER: Ahn H 

PROVIDER: S-EPMC9386450 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature