Forme Fruste Keratoconus Detection with OCT Corneal Topography Using Artificial Intelligence Algorithms

Eugénie Mourgues; Virgile Saunier; David Smadja; David Touboul; Valentine Saunier

doi:10.1097/j.jcrs.0000000000001542

Forme Fruste Keratoconus Detection with OCT Corneal Topography Using Artificial Intelligence Algorithms

J Cataract Refract Surg. 2024 Sep 3. doi: 10.1097/j.jcrs.0000000000001542. Online ahead of print.

Authors

Eugénie Mourgues¹, Virgile Saunier², David Smadja², David Touboul¹, Valentine Saunier¹

Affiliations

¹ Ophthalmology Unit, University Hospital Bordeaux, France.
² Department of Ophthalmology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Israel.

PMID: 39223730
DOI: 10.1097/j.jcrs.0000000000001542

Abstract

Purpose: Distinguishing early Keratoconus (KC) from normal corneas is challenging owing to their striking similarities. The aim of our study was to identify discriminating parameters to differentiate a normal cornea from a Form Fruste Keratoconus (FFKC) with the Swept-Source (SS) OCT-topography CASIA 2 (Tomey,Japan) using machine learning artificial intelligence algorithms.

Setting: The study was monocentric, carried out in Bordeaux.

Design: This was a retrospective study, case control.

Methods: Three groups were included: KC group (108 eyes), FFKC (88 eyes) and normal corneas (162 eyes).The data were analyzed and processed using the Dataiku data science platform. Machine learning models (Random Forest, Logistic Regression) were used to develop a multiclass classifier for automated early KC detection. The models were trained using a training database and tested using a test database. Then algorithms were compared to the Ectasia Screening Index (ESI), which is an OCT-topography inherent screening score for ectasia.

Results: The Logistic Regression (LR), and Random Forest (RF) detected FFKC with an AUC of 0,99, and 0,98 respectively. The sensitivities of LR (100%), RF (84%) were better than the ESI (28%) for the diagnosis of FFKC. However, ESI has a maximum specificity (100%) compared to the RL (100%) and 90% for RF.

Conclusion: This study identified discriminating topographic parameters to be considered in refractive surgery screening on SS-OCT CASIA 2. We developed an algorithm capable of classifying normal eyes versus FFKC cases, with improved performance compared to the ESI score.