Primary Angle Closure Disease (PACD) is the most common cause of vision impairment worldwide. Early treatment is crucial in preventing vision loss. Anterior Segment Optical Coherence Tomography (AS-OCT) is an imaging modality that produces images of anterior structures such as the Anterior Chamber Angle (ACA) and the scleral spur. However, adoption of the AS-OCT modality has been gradual due to AS-OCT analysis not being standardized and inefficient. medical professionals typically must annotate each image by hand using proprietary software and use expert knowledge to diagnose PACD based on the key features annotated. Using an imaging-informatics-based approach on a dataset of almost 1200 images, we have developed a DICOM-compatible system to streamline and standardize AS-OCT analysis, utilizing a HIPAA-compliant database requiring a secure login to protect patient privacy. Previously, we developed a streamlined approach towards annotating key features in AS-OCT images which will be used to validate the results produced by SimpleMind – an open-source software framework supporting deep neural networks with machine learning and automatic parameter tuning. SimpleMind is integrated into the system to increase the efficiency of analyzing AS-OCT images and eliminate the need to annotate images for clinical diagnosis. The goal is to develop a comprehensive and robust hybrid system combining traditional and deep learning image processing methods to detect the scleral spur and estimate a measure of the anterior chamber angle’s degree of openness from AS-OCT images. This paper presents a hybrid method of determining the ACA boundary region to produce an angle measurement that can help indicate PACD.
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