Abstract

Photorefractive Keratectomy (PRK) is a widely used laser-assisted refractive surgical technique. While generally safe, in some cases it leads to subepithelial inflammation or fibrosis. We here present a robust, machine learning based algorithm for the detection of fibrosis based on spectral domain optical coherence tomography (SD-OCT) images recorded in vivo on standard clinical devices.

The images first undergo a treatment by a previously developed algorithm for standardisation. The analysis of the pre-treated images allows the extraction of quantitative parameters characterizing the transparency of human corneas. We here propose an extension of this work.

Our model is based on 9 morphological quantifiers of the corneal epithelium and in particular of Bowman's layer. In a first step it is trained on SD-OCT images of corneas presenting Fuchs dystrophy, which causes similar symptoms of fibrosis. We trained a Random Forest model for the classification of corneas into "healthy" and "pathological" classes resulting in a classification accuracy (or success rate) of 97%.

The transfer of this same model to images from patients who have undergone photorefractive keratectomy (PRK) surgery shows that the model output for probability of healthy classification provides a quantified indicator of corneal healing in the post-operative follow-up. The sensitivity of this probability was studied using repeatability data. We could therefore demonstrate the ability of artificial intelligence to detect sub-epithelial scars identified by clinicians as the origin of post-operative visual haze.

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