Classification of healthy and pathological human corneas by the analysis of clinical SD-OCT images using machine learning

Maëlle Vilbert; Corentin Soubeiran; Benjamin Memmi; Cristina Georgeon; Vincent Borderie; Anatole Chessel; Karsten Plamann

doi:10.1117/12.2670703

11 August 2023 Classification of healthy and pathological human corneas by the analysis of clinical SD-OCT images using machine learning

Maëlle Vilbert, Corentin Soubeiran, Benjamin Memmi, Cristina Georgeon, Vincent Borderie, Anatole Chessel, Karsten Plamann

Author Affiliations +

Proceedings Volume 12632, Optical Coherence Imaging Techniques and Imaging in Scattering Media V; 126320T (2023) https://doi.org/10.1117/12.2670703
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

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 allow 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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Maëlle Vilbert, Corentin Soubeiran, Benjamin Memmi, Cristina Georgeon, Vincent Borderie, Anatole Chessel, and Karsten Plamann "Classification of healthy and pathological human corneas by the analysis of clinical SD-OCT images using machine learning", Proc. SPIE 12632, Optical Coherence Imaging Techniques and Imaging in Scattering Media V, 126320T (11 August 2023); https://doi.org/10.1117/12.2670703

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KEYWORDS

Cornea

Image classification

Machine learning

Algorithm development

Education and training

Evolutionary algorithms

Standards development

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