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

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.

EVALUATION OF THE INCLUSION OF SPECTRAL DOMAIN OPTICAL COHERENCE TOMOGRAPHY IN A TELEMEDICINE DIABETIC RETINOPATHY SCREENING PROGRAM: A Real Clinical Practice.

Purpose: To evaluate whether combining spectral domain optical coherence tomography with monoscopic fundus photography using a nonmydriatic camera (MFP-NMC) improves the accuracy of diabetic macular edema (DME) referrals in a teleophthalmology diabetic retinopathy screening program.

Methods: We conducted a cross-sectional study with all diabetic patients aged 18 years or older who attended screening from September 2016 to December 2017. We assessed DME according to the three MFP-NMC and the four spectral domain optical coherence tomography criteria.