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Published on: October 21, 2022
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).
Purpose:
To differentiate a normal cornea from a forme fruste keratoconus (FFKC) with the swept-source optical coherence tomography (SS-OCT) topography CASIA 2 using machine learning artificial intelligence algorithms.
Setting:
Monocentric, performed in CHU Bordeaux, Bordeaux, France.
Design:
Retrospective case-control.
Methods:
3 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 [RF], logistic regression [LR]) 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 with the Ectasia Screening Index (ESI), which is an OCT-topography inherent screening score for ectasia.
Results:
The LR and RF detected FFKC with an area under the curve of 0.99 and 0.98, respectively. The sensitivities of LR (100%) and RF (84%) were better than the ESI (28%) for the diagnosis of FFKC. However, ESI has a maximum specificity (100%) compared with the LR (100%) and 90% for RF.
Conclusions:
This study identified discriminating topographic parameters to be considered in refractive surgery screening on SS-OCT CASIA 2. An algorithm capable of classifying normal eyes vs FFKC cases was developed, with improved performance compared with the ESI score.
