Deep learning to detect macular atrophy in wet age-related macular degeneration using optical coherence tomography.

ABSTRACT: Here, we have developed a deep learning method to fully automatically detect and quantify six main clinically relevant atrophic features associated with macular atrophy (MA) using optical coherence tomography (OCT) analysis of patients with wet age-related macular degeneration (AMD). The development of MA in patients with AMD results in irreversible blindness, and there is currently no effective method of early diagnosis of this condition, despite the recent development of unique treatments. Using OCT dataset of a total of 2211 B-scans from 45 volumetric scans of 8 patients, a convolutional neural network using one-against-all strategy was trained to present all six atrophic features followed by a validation to evaluate the performance of the models. The model predictive performance has ac

SUBMITTER: Wei W 

PROVIDER: S-EPMC10203346 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature