Modular multi-task deep learning framework for prediction of treatment initiation in neovascular age-related macular...

The purpose of this study is to develop a fully automated system for determining the optimal time to begin anti-vascular endothelial growth factor (anti-VEGF) treatment, based on retinal changes observed in optical coherence tomography (OCT) images and visual acuity (VA) scores.

This retrospective study included two cohorts: patients with intermediate dry age-related macular degeneration (AMD) who did not progress to neovascular AMD (NVAMD) and those who progressed and received anti-VEGF treatment. Subjects had ≥3 consecutive visits. Two tasks were defined: (1) develop a deep learning (DL) model to identify the need for anti-VEGF treatment and (2) estimate outer retina thickness (ORT) from Bruch's membrane to the outer plexiform layer as an imaging biomarker. A convolutional neural network extracted OCT features related to ORT, and two long short-term memory models were trained on separate datasets to address Task I and II treatments.

A total of 122 patients (207 eyes) were included: 49 in dataset 1 (69 eyes; mean (SD) age 84 (8); 25 (51%) female) and 100 in dataset 2 (138 eyes; 82 (7); 54 (54%) female). Task I performance was evaluated by the model's alignment with clinical decisions on anti-VEGF initiation, and demonstrated area under the receiver operating curve of 0.73 (95% CI 0.59 to 0.87), area under the precision-recall curve of 0.51 (95% CI 0.35 to 0.66), an accuracy of 0.87 (95% CI 0.76 to 0.97), a precision of 0.73 (95% CI 0.59 to 0.87), a sensitivity of 0.69 (95% CI 0.54 to 0.83) and a specificity of 0.92 (95% CI 0.84 to 1.00). Task II achieved NMAE 0.12 (95% CI 0.04 to 0.20).

This DL framework provides a fully automated approach for determining optimal treatment timing, supporting earlier intervention to preserve VA in NVAMD patients.

An accurate DL framework that can help with identifying the best time to initiate therapy for NVAMD.