Deep Learning-Based Segmentation of Geographic Atrophy: A Multi-Center, Multi-Device Validation in a Real-World Clinical Cohort

Background: To report a deep learning-based algorithm for automated segmentation of geographic atrophy (GA) among patients with age-related macular degeneration (AMD). Methods: Validation of a deep learning algorithm was performed using optical coherence tomography (OCT) images from patients in routine clinical care diagnosed with GA, with and without concurrent nAMD. For model construction, a 3D U-Net architecture was used with the output modified to generate a 2D mask. Accuracy of the model was assessed relative to the manual labeling of GA with the Dice similarity coefficient (DSC) and correlation r² scores. Results: The OCT data set included 367 scans from the Spectralis (Heidelberg, Germany) from 55 eyes in 33 subjects; 267 (73%) scans had concurrent nAMD. In parallel, 348 scans were collected using the Cirrus (Zeiss), from 348 eyes in 326 subjects; 101 (29%) scans had concurrent nAMD. For Spectralis data, the mean DSC score was 0.83 and r² was 0.91. For Cirrus data, the mean DSC score was 0.82 and r² was 0.88. Conclusions: The reported deep learning algorithm demonstrated strong agreement with manual grading of GA secondary to AMD on the OCT data set from routine clinical practice. The model performed well across two OCT devices as well as amongst patients with GA with concurrent nAMD, suggesting applicability in the clinical space.