Improved Glaucoma Monitoring Using Artificial-Intelligence Enabled Dashboard

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Detecting functional and structural loss due to glaucoma is critical to making treatment decisions with the goal of preserving vision and maintaining quality of life. However, most of the approaches for glaucoma assessment through visual fields (VFs) or optical coherence tomography (OCT) measurements have several limitations that poses critical challenge to their clinical utility. Identifying glaucoma-induced changes from a sequence of VF or OCT data is challenging either if the patients is in the early stages of the disease with subtle manifested structural and functional signs or if the patients are in the later stages of the disease with significant VF variability and OCT flooring effect. A major limitation of the current glaucoma monitoring techniques is that they generate a binary outcome of whether the glaucoma is worsening or not while current high-throughput data (e.g., OCT) has more information than a binary outcome. Another major drawback of some of these approaches is that they rely on traditional paradigms for progression detection such as linear regression. However, rates of glaucomatous progression may be non-linear and rapid, particularly during the later stages of the disease. Another limitation is that ad-hoc rules are adopted to define glaucoma progression while objective criteria are required to define thresholds for progression. Finally, a major deficiency of most of these methods is that they lack advanced visualization and interpretation. We propose to address these limitations by developing artificial intelligence (AI)-enabled visualization tools for effectively monitoring the functional and structural loss in patients with glaucoma. This approach provides qualitative and quantitative means to monitor 1) global visual functional and structural worsening, 2) extent of loss in hemifields, and 3) local patterns of functional and structural loss on advanced 2-D visualization tools. To achieve these objectives, we have assembled a team of interdisciplinary experts with access to large clinically annotated glaucoma data. The central hypothesis of this proposal is that advanced interpretable machine learning applied to a complete profile of VFs in all test locations (e.g., 54 in 24-2 system) and OCT-derived measurements of retinal nerve fiber layer (RNFL) (e.g., 768 A-scans around the optic disc and 7 global sectoral regions) can objectively and automatically learn and quantify the most important features, yielding a more specific and sensitive means for monitoring of glaucoma worsening than current subjectively-specified or statistically-identified approaches. We also hypothesize that machine learning can provide interpretable models with several layers of glaucoma knowledge that may provide a promising complement to current glaucoma assessment tests. Our proposed studies may offer substantial improvements in prognosis and management of glaucoma through effective use of analysis and visualization to improve glaucoma management and making more informed treatment options.