Automated Identification and Segmentation of Ellipsoid Zone At-Risk Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD

The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of (EZ) to study progression in nonexudative age-related macular degeneration (AMD). Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic at...

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Bibliographic Details
Published in:Diagnostics (Basel) 2023-03, Vol.13 (6), p.1178
Main Authors: Kalra, Gagan, Cetin, Hasan, Whitney, Jon, Yordi, Sari, Cakir, Yavuz, McConville, Conor, Whitmore, Victoria, Bonnay, Michelle, Reese, Jamie L, Srivastava, Sunil K, Ehlers, Justis P
Format: Article
Language:English
Subjects:
age-related macular degeneration
Atrophy
automated feature segmentation
Automation
Biomarkers
Clinical trials
Datasets
Deep learning
ellipsoid zone integrity
FDA approval
Macular degeneration
Patients
photoreceptor damage
Photoreceptors
quantitative optical coherence tomography
Review boards
Statistical data
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Summary:The development and testing of a deep learning (DL)-based approach for detection and measurement of regions of (EZ) to study progression in nonexudative age-related macular degeneration (AMD). Used in DL model training and testing were 341 subjects with nonexudative AMD with or without geographic atrophy (GA). An independent dataset of 120 subjects were used for testing model performance for prediction of GA progression. Accuracy, specificity, sensitivity, and intraclass correlation coefficient (ICC) for DL-based percentage area measurement was calculated. Random forest-based feature ranking of was compared to previously validated quantitative OCT-based biomarkers. The model achieved a detection accuracy of 99% (sensitivity = 99%; specificity = 100%) for . Automatic measurement achieved an accuracy of 90% (sensitivity = 90%; specificity = 84%) and the ICC compared to ground truth was high (0.83). In the independent dataset, higher baseline mean correlated with higher progression to GA at year 5 ( < 0.001). was a top ranked feature in the random forest assessment for GA prediction. This report describes a novel high performance DL-based model for the detection and measurement of . This biomarker showed promising results in predicting progression in nonexudative AMD patients.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics13061178