Automated Identification and Segmentation of Using Deep Learning on SD-OCT for Predicting Progression in Dry AMD.

BACKGROUND

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).

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.