ArticleThe British journal of ophthalmology2026
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.
Article in The British journal of ophthalmology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThe 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.
methodsThis 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.
resultsA 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).
conclusionsThis 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.
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