Modular multi-task deep learning framework for prediction of treatment initiation in neovascular age-related macular degeneration modular AI for NVAMD treatment initiation study-MANTIS.

PURPOSE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: This DL framework provides a fully automated approach for determining optimal treatment timing, supporting earlier intervention to preserve VA in NVAMD patients. TRANSITIONAL RELEVANCE: An accurate DL framework that can help with identifying the best time to initiate therapy for NVAMD.