Risk Classification for Progression to Subfoveal Geographic Atrophy in Dry Age-Related Macular Degeneration Using Machine Learning–Enabled Outer Retinal Feature Extraction
Kübra Sarıcı, Joseph R. Abraham, Duriye Damla Sevgi, Leina Lunasco, Sunil K. Srivastava, Jon Whitney, Hasan Cetin, Annapurna Hanumanthu, Jordan M. Bell, Jamie Reese, Justis P. Ehlers
Abstract
BACKGROUND AND OBJECTIVE: To evaluate the utility of spectral-domain optical coherence tomography biomarkers to predict the development of subfoveal geographic atrophy (sfGA). PATIENTS AND METHODS: This was a retrospective cohort analysis including 137 individuals with dry age-related macular degeneration without sfGA with 5 years of follow-up. Multiple spectral-domain optical coherence tomography quantitative metrics were generated, including ellipsoid zone (EZ) integrity and subretinal pigment epithelium (sub-RPE) compartment features. RESULTS: Reduced mean EZ-RPE central subfield thickness and increased sub-RPE compartment thickness were significantly different between sfGA convertors and nonconvertors at baseline in both 2-year and 5-year sfGA risk assessment. Longitudinal change assessment showed a significantly higher degradation of EZ integrity in sfGA convertors. The predictive performance of a machine learning classification model based on 5-year and 2-year risk conversion to sfGA demonstrated an area under the receiver operating characteristic curve of 0.92 ± 0.06 and 0.96 ± 0.04, respectively. CONCLUSIONS: Quantitative outer retinal and sub-RPE feature assessment using a machine learning–enabled retinal segmentation platform provides multiple parameters that are associated with progression to sfGA. [ Ophthalmic Surg Lasers Imaging . 2022;53:31–39.]