Primary open angle glaucoma (POAG) is a leading cause of blindness in the United States and worldwide. It is estimated that over 2.2 million Americans suffer from POAG and that over 130,000 are legally blind from the disease. As the population ages, the number of people with POAG in the United States will increase to over 3.3 million in 2020 and worldwide to an estimated 111.8 million by 2040. POAG is a progressive disease associated with characteristic functional and structural changes that clinicians use to diagnose and monitor the disease. Over the past several years, spectral domain optical coherent tomography (SDOCT) has become the standard tool for measuring structure in POAG. This 3D imaging modality provides a wealth of information about retinal structure and POAG-related retinal layers. This large amount of data is hard for clinicians to interpret and use effectively to help guide treatment decisions. Instead, summary metrics such as average layer thicknesses are used to reduce SDOCT images to a handful of values. While these metrics are useful, they can be difficult to interpret and they throwaway important information regarding voxel intensity and texture, relationships across retinal layers, and the overall 3D structure of the retina. Relying too heavily on these metrics limits our ability to gain a deeper understanding structural contributions to POAG, the relationship between structure and visual function, and how structural (and functional) changes progress in POAG. Recent advances in artificial intelligence and deep learning, however, offer new data-driven tools and techniques to interpret 3D SDOCT images and learn from the large SDOCT datasets being collected in clinics around the world. This proposal will apply state-of-the-art deep learning techniques to 3D SDOCT data in order to (1) develop more accurate POAG detection tools, (2) reveal structure-function relationships, and (3) predict structural and functional progression in POAG. This proposal also details a training plan to help the PI transition from a postdoctoral scholar to an independent researcher. The mentored phase of this award will be supervised by the primary mentor, Dr. Linda Zangwill, and a multidisciplinary mentoring team including Dr. Robert Weinreb (Ophthalmology), Dr. David Kriegman (Computer Science and Engineering), and Dr. Armin Schwartzman (Biostatistics). Performing the proposed research, formal coursework, and mentored career development will the provide the PI with highly sought- after skills and experience to help ensure a successful transition into independence.

Public Health Relevance

Three-dimensional imaging techniques such as optical coherence tomography have become an essential tool in the clinical care of glaucoma and other eye diseases. These imaging techniques provide clinicians with huge amounts of structural information, but interpreting the data and using it effectively to improve outcomes remains challenging in clinical glaucoma management. This project will improve patient care by applying powerful deep learning techniques to provide clinicians with critical decision support information to more accurately detect glaucoma, reveal associations between structure and visual function, and predict glaucoma progression.

National Institute of Health (NIH)
National Eye Institute (NEI)
Career Transition Award (K99)
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Special Emphasis Panel (ZEY1)
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Agarwal, Neeraj
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University of California, San Diego
Schools of Medicine
La Jolla
United States
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