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.
We describe an automated algorithm allowing extraction of quantitative corneal transparency parameters with clinical Spectral-Domain Optical Coherence Tomography (SD-OCT). Our algorithm employs a novel pre-processing procedure to standardize SD-OCT image analysis and to numerically correct common instrumental artifacts before extracting mean intensity stromal-depth (z) profiles over a 6-mm-wide corneal area. The z-profiles are analyzed using our previously developed objective method deriving quantitative transparency parameters which are directly related to the physics of light propagation in tissues. Tissular heterogeneity is quantified by the Birge ratio, Br; for homogeneous tissues (i.e., Br~1), the photon mean-free path (ls) may be determined. Images of 83 normal corneas (ages 22–50 years) from a standard SD-OCT device (RTVue-XR Avanti, Optovue Inc.) were processed to establish a normative dataset of transparency values. After confirming stromal homogeneity (Br⪅10), we measured a median ls of 570 μm (interdecile range: 270–2400 μm). Considering corneal thicknesses, this may be translated into a median fraction of transmitted (coherent) light Tcoh(stroma) of 51% (interdecile range: 22–83%). Excluding images with central saturation artifact raised our median Tcoh(stroma) to 73% (inter-decile range: 34–84%). These transparency values are slightly lower than previously reported, which we attribute to the detection configuration of SD-OCT with a relatively small and selective acceptance angle. No statistically significant correlation between transparency and age or thickness was found. Our algorithm provides robust and quantitative measurements of corneal transparency from standard SD-OCT images with sufficient quality and addresses the demand for such an objective means in the clinical setting.
Line-field confocal optical coherence tomography (LC-OCT) is an alternative to conventional OCT that combines OCT and confocal microscopy. This technique gives access to three-dimensional (3D) images with a micrometer resolution in the three spatial directions and enhances signal from the deepest layers within the material. After an experimental determination of the device characteristics, the technique is used for the investigation of16th to 18th century fragments of gilt leathers wall-hangings. In these objects, the various layers within the varnish can be identified and the effect of a restoration treatment can be observed to validate the varnish removal process.
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