Deep Learning Applications in Anterior Segment OCT

This technical report discusses the application of deep learning (DL) algorithms in anterior segment optical coherence tomography (AS-OCT) image analysis, focusing on optimizing biometric measurements and scleral spur detection. AS-OCT has become a vital tool in ophthalmology, providing high-resolution images of the anterior chamber. However, traditional methods for extracting quantitative data are often time-consuming and variable. The report outlines how DL can enhance accuracy and efficiency in these processes, thereby facilitating improved clinical workflows. It presents findings from a study that validated DL algorithms for automated scleral spur detection, demonstrating their capability to achieve expert-level performance. The report details the methodology, including the use of convolutional neural networks (CNNs) and the training of algorithms on a substantial dataset. Results indicate that DL algorithms can provide reliable biometric measurements comparable to those of human graders, particularly in challenging cases such as narrow-angle assessments. The integration of DL into AS-OCT analysis is positioned to transform clinical practices and enhance patient care.