Author:
Sarici Kubra,Abraham Joseph R.,Sevgi Duriye Damla,Lunasco Leina,Srivastava Sunil K.,Whitney Jon,Cetin Hasan,Hanumanthu Annapurna,Bell Jordan M.,Reese Jamie L.,Ehlers Justis P.
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.]
Cited by
8 articles.
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