Primary angle closure glaucoma (PACG), the most severe form of primary angle closure disease (PACD), is a leading cause of permanent vision loss and blindness worldwide. Gonioscopy is the current clinical standard for evaluating the anterior chamber angle (ACA) and managing PACD. However, gonioscopy is qualitative, requires considerable expertise, and is poorly predictive of which patients will benefit from treatment with laser or surgery. These shortcomings limit the utility of gonioscopy for evaluating patients with angle closure and optimizing their clinical care. Therefore, there is an urgent need for standardized quantitative methods that guide the evaluation and management of patients with angle closure. Anterior segment optical coherence tomography (AS-OCT) is a novel non-contact form of in vivo ocular imaging that produces high-resolution images and quantitative measurements of ocular biometric parameters, some of which are known risk factors for PACD. AS-OCT could be developed as a modern alternative to gonioscopy for evaluating the ACA and guiding management of angle closure.

However, the clinical utility of AS-OCT is currently limited by two factors: 1) it is unclear which biometric parameters are most strongly associated with angle closure and predict response to laser treatment, and 2) quantitative analysis of AS-OCT images is only semi-automated; each image requires a trained grader manually identify a specific anatomical structure, the scleral spur, before biometric risk factors for angle closure can be assessed. To address these limitations, we propose two studies: 1) identify which ocular biometric parameters determine ACA width and response to laser treatment for PACD, and; 2) automate quantitative analysis of biometric risk factors for PACD by applying deep learning methods to AS-OCT images. We believe quantitative AS-OCT measurements have great potential to advance the clinical management of patients at risk for PACG and reduce the incidence of PACG-related vision loss.

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