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. 2024 Dec 1;50(12):1247-1253.
doi: 10.1097/j.jcrs.0000000000001542.

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

Affiliations

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

Eugénie Mourgues et al. J Cataract Refract Surg. .

Abstract

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.

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Figures

Figure 1.
Figure 1.
The yellow square shows the parameters used in logistic regression and random forest for the diagnosis of FFKC. FFKC = forme fruste keratoconus
Figure 2.
Figure 2.
The histogram represents the data selected by the random forest algorithm and their importance in distinguishing FFKC from a healthy eye. The most discriminating data are grouped in this figure. Aberr Ant = aberration anterior; Aberr Post = aberration posterior; CTP = corneal thinnest point; EPHy = elevation posterior highest offset y in millimeters; FFKC = forme fruste keratoconus; FIAP AP 6 = Fourier index axial power asymmetry posterior within 6 mm; IPI Y = instantaneous posterior offset set Y in millimeters; Pachy T = pachymetry temporal

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