Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.

ABSTRACT: Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection bias, we stacked convolutional and recurrent neural networks (CNN-RNN) to analyze complete optical coherence tomography (OCT) cubes in a row and predict diabetic macular edema (DME), in a real-world diabetic retinopathy screening program. A retrospective cohort study was carried out. Throughout 4-years, 5314 OCT cubes from 4408 subjects who attended to the diabetic retinopathy (DR) screening program were included. We arranged twenty-two (22) pre-trained CNNs in parallel with a bidirectional RNN layer stacked at the bottom, allowing the model to

SUBMITTER: Rodriguez-Miguel A 

PROVIDER: S-EPMC11291805 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature