Maëlle Vilbert, Corentin Soubeiran, Benjamin Memmi, C. Georgeon, V. Borderie, A. Chessel, K. Plamann
{"title":"Classification of healthy and pathological human corneas by the analysis of clinical SD-OCT images using machine learning","authors":"Maëlle Vilbert, Corentin Soubeiran, Benjamin Memmi, C. Georgeon, V. Borderie, A. Chessel, K. Plamann","doi":"10.1117/12.2670703","DOIUrl":null,"url":null,"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.","PeriodicalId":278089,"journal":{"name":"European Conference on Biomedical Optics","volume":"64 4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Conference on Biomedical Optics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2670703","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
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.