ABSTRACT
Purpose: To establish optical coherence tomography (OCT)/angiography (OCTA) parameter ranges for healthy eyes (HE) and glaucomatous eyes (GE) for a North Texas based population; to develop a machine learning (ML) tool and to identify the most accurate diagnostic parameters for clinical glaucoma diagnosis. Patients and Methods: In this retrospective cross-sectional study, we included 1371 eligible eyes, 462 HE and 909 GE (377 ocular hypertension, 160 mild, 156 moderate, 216 severe), from 735 subjects. Demographic data and full OCTA parameters were collected. A Kruskal-Wallis test was used to produce the normative database. Models were trained to solve a two-class problem (HE vs GE) and four-class problem (HE vs mild vs moderate vs severe GE). A rigorous nested, stratified, group, 5×10 fold cross-validation strategy was applied to partition the data. Six ML algorithms were compared using classical and deep learning approaches. Over 2500 ML models were optimized using random search, with performance compared using mean validation accuracy. Final performance was reported on held-out test data using accuracy and F1 score. Decision trees and feature importance were produced for the final model. Results: We found differences across glaucoma severities for age, gender, hypertension, Black and Asian race, and all OCTA parameters, except foveal avascular zone area and perimeter (p<0.05). The XGBoost algorithm achieved the highest test performance for both the two-class (F1 score 83.8%; accuracy 83.9%; standard deviation 0.03%) and four-class (F1 score 62.4%; accuracy 71.3%; standard deviation 0.013%) problem. A set of interpretable decision trees provided the most important predictors of the final model; inferior temporal and inferior hemisphere vessel density and peripapillary retinal nerve fiber layer thickness were identified as key diagnostic parameters. Conclusion: This study established a normative database for our North Texas based population and created ML tools utilizing OCT/A that may aid clinicians in glaucoma management.
ABSTRACT
We describe a 51-year-old Hispanic female with nail-patella syndrome (NPS), a rare genetic disease with a wide range of systemic features such as nail dysplasia and finger abnormalities, elbow webbing, iliac horn, patellar subluxation, and proteinuria. Some patients additionally have a history of glaucoma and other ocular features such as thick central corneal thickness, Lester's sign, prominent iris processes, and optic nerve cupping. Our patient had a history of glaucoma suspicion, prominent iris processes, increased cup to disc ratios, tilted optic discs, and tigroid fundi. In addition, we report optical coherence tomography angiography (OCTA) findings of focal areas of poor vessel densities in the macular and circumpapillary regions of both eyes, suggesting early compromised vascular supplies to these areas. Our OCTA findings (which include both structural and vascular details of retina and optic nerve) lend support to the use of this technology in patients with NPS.
