A Comprehensive Strategy to Detect Glaucoma Worsening Earlier and With Fewer Tests

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ABSTRACT This is an application for a K23 Mentored Patient-Oriented Research Career Development Award. The goal of this proposal is to provide the candidate with the advanced skills needed to establish an independent research program in the area of glaucoma diagnostic testing with special expertise in test error correction and predictive modeling of future glaucoma outcomes. To facilitate this long-term goal, in the current proposal, the candidate’s main research goal is to reduce the time and number of tests necessary to detect glaucoma worsening by (1) correcting for errors in previously obtained visual field (VF) and peripapillary optical coherence tomography (OCT) tests by using multilevel models with Bayesian analysis (MLB) and generative adversarial networks (GAN) (2) stratifying eyes at high and low risk for rapid glaucoma worsening at the baseline clinical visit using deep convolutional neural networks (DCNN). These aims are based on high quality preliminary data which show that: (1) the effect of VF reliability metrics and OCT signal strength on test error can be quantified and thus corrected for and (2) machine learning methods can predict risk of future VF progression with fair accuracy with baseline visit VF data alone and therefore adding structural (OCT) and clinical information from the baseline visit is likely to improve model accuracy. The main hypotheses of the proposed research aims are (1) correcting for test errors with MLB and GAN will reduce the time needed to detect worsening by 10 and 20% respectively (2) combining baseline visit structural (OCT), functional (VF) and clinical data as inputs into DCNNs will allow us to achieve an area under the receiver operating curve of at least 0.8 at predicting the risk of future rapid glaucoma worsening. The candidate proposes a comprehensive training plan, combining formal coursework, meetings, seminars and workshops overseen by his diverse group of mentors. Specific training goals include: (1) Receiving training in multi-level regression modeling and Bayesian analysis techniques. (2) Becoming adept at data science with a special emphasis on learning Python for data extraction, manipulation and analysis. (3) Furthering knowledge of machine learning techniques with a specific emphasis on deep learning including DCNNs and GANs. (4) Continuing training in the ethical and responsible conduct of research. The training plan will be executed in coordination with the set of research activities mentioned above. Results from this research proposal will be used to develop a subsequent R01 research proposal that will facilitate the candidate’s transition to an independent researcher.