Automatic corneal transplants thickness and area measurement using deep learning

For several decades after corneal transplantation was performed for the first time, studies to predict the success of corneal transplantation have been conducted. To obtain a successful corneal transplantation, various factors other than biocompatibility between the donor cornea and the transplant recipient's eye must be satisfied. Therefore, various studies are being conducted to develop an artificial cornea that does not require a donor. One of the important indicators contributing to the success of corneal transplantation is measurement of corneal thickness (CT) after corneal transplantation. In previous studies, to measure the CT and transplanted cornea, partial CT measurement using an algorithm was mainly performed in optical coherence tomography (OCT) images. However, a single algorithm eventually has limitations in determining the suitability of the entire transplanted cornea. In this study, we automatically segmented the region of the artificial cornea implanted in the rabbit cornea through U-Net based models, and based on this, we measured and analyzed the three-dimensional total thickness of the conventional cornea and the artificial cornea. Our results suggest that the thickness of the transplanted and existing corneas can be automatically measured over time to provide information as an indicator for determining the success of corneal transplants.