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

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Principal Investigator: Jithin  Yohannan
Organization: JOHNS HOPKINS UNIVERSITY
Fiscal Year: 2024
Award: $191,187
Funding agency: National Eye Institute

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.

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