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Predicting Ectasia Risk From Evaluated Corneal Tomography

An AI-driven tool to accurately predict the risk of corneal ectasia development with and without refractive surgery.

AI analysis of multiple factors from the Holladay Report of the OCULUS Pentacam® produces risk results rooted in machine learning models.

The algorithm is trained on results over time to detect sensitive changes that are associated withe eventual development of corneal ectasia in both cases from the general population as well as post-refractive surgery.

Objectives

To create an accurate, repeatable, and continuously improve machine-learning based tool for the prediction of corneal ectasia development. This will assist clinicians in the diagnosis of corneal ectasia as well as screening for refractive surgery candidates.

Team

Co-creators Matthew Hirabayashi MD and Gurpal Virdi MD lead an engineering team to develop the test.
They are closely advised by Jack Holladay MD MSEE FACS

Gurpal Virdi MD

Matt Hirabayashi MD

Jack Holladay MD MSEE FACS

Progress

The machine-learning algorithm is currently in development.