Risk Classification for Progression to Subfoveal Geographic Atrophy in Dry Age-Related Macular Degeneration Using Machine Learning-Enabled Outer Retinal Feature Extraction

Risk Classification for Progression to Subfoveal Geographic Atrophy in Dry Age-Related Macular Degeneration Using Machine Learning-Enabled Outer Retinal Feature Extraction

BACKGROUND AND OBJECTIVE: To evaluate the utility of spectral-domain optical coherence tomography biomarkers to predict the development of subfoveal geographic atrophy (sfGA)

Détails bibliographiques
Publié dans:Ophthalmic surgery, lasers & imaging retina. - 2013. - 53(2022), 1 vom: 21. Jan., Seite 31-39
Auteur principal: Sarici, Kubra (Auteur)
Autres auteurs: 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
Format: Article en ligne
Langue:English
Publié: 2022
Accès à la collection:Ophthalmic surgery, lasers & imaging retina
Sujets:Journal Article Research Support, N.I.H., Extramural
Description
Résumé: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.]
Description:Date Completed 08.04.2022
Date Revised 08.04.2022
published: Print-Electronic
Citation Status MEDLINE
ISSN:2325-8179
DOI:10.3928/23258160-20211210-01