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Full-Field Optical Coherence Microscopy for Histology-Like Analysis of Stromal Features in Corneal Grafts

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Forme fruste keratoconus detection with OCT corneal topography using artificial intelligence algorithms.

Eugénie Mourgues¹, Virgile Saunier, David Smadja

¹From the Ophthalmology Unit, University Hospital Bordeaux, Bordeaux, France (Mourgues, Saunier, Touboul, Valentine Saunier); Department of Ophthalmology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel (Smadja).

Journal of Cataract and Refractive Surgery

|September 3, 2024

Summary

Machine learning algorithms effectively differentiate normal corneas from forme fruste keratoconus (FFKC) using swept-source optical coherence tomography (SS-OCT) topography. This AI approach shows improved sensitivity for early keratoconus detection compared to the Ectasia Screening Index (ESI).

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Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Keratoconus (KC) is a progressive corneal disease.
Early detection of forme fruste keratoconus (FFKC) is crucial for preventing vision loss and guiding refractive surgery decisions.
Current diagnostic tools have limitations in identifying early-stage KC.

Purpose of the Study:

To develop and evaluate machine learning (ML) algorithms for differentiating normal corneas from FFKC using swept-source optical coherence tomography (SS-OCT) topography.
To compare the performance of ML models against the Ectasia Screening Index (ESI).

Main Methods:

Retrospective analysis of 108 KC, 88 FFKC, and 162 normal cornea eyes using SS-OCT CASIA 2.
Data processed using Dataiku; ML models (random forest, logistic regression) trained for multiclass classification.
Algorithms compared with the ESI score for ectasia screening.

Main Results:

Logistic Regression (LR) and Random Forest (RF) models demonstrated high accuracy in detecting FFKC (AUC 0.99 and 0.98).
LR achieved 100% sensitivity, outperforming ESI (28%) for FFKC diagnosis.
ESI showed higher specificity (100%) than RF (90%), while LR maintained 100% specificity.

Conclusions:

Discriminating topographic parameters were identified for refractive surgery screening using SS-OCT CASIA 2.
A novel algorithm was developed for classifying normal versus FFKC eyes.
The developed ML algorithm offers superior performance for early KC detection compared to the ESI score.