Early Detection of Macular Atrophy Automated Through 2D and 3D Unet Deep Learning

Abstract

Macular atrophy (MA) is an irreversible endpoint of age-related macular degeneration (AMD), which is the leading cause of blindness in the world. Early detection is therefore an unmet need. We have developed a novel automated method to identify MA in patients undergoing follow-up with optical coherence tomography (OCT) for AMD based on the combination of 2D and 3D Unet architecture. Our automated detection of MA relies on specific structural changes in OCT, including six established atrophy-associated lesions. Using 1241 volumetric OCTs from 125 eyes (89 patients), the performance of this combination Unet architecture is extremely encouraging, with a mean dice similarity coefficient score of 0.90 ± 0.14 and a mean F1 score of 0.89 ± 0.14. These promising results have indicated superiority when compared to human graders, with a mean similarity of 0.71 ± 0.27. We believe this deep learning-aided tool would be useful to monitor patients with AMD, enabling the early detection of MA and supporting clinical decisions.

Keywords

• macular atrophy
• deep learning
• optical coherence tomography