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

Abstract

Purpose The purpose of this study is to develop a fully automated system for determining the optimal time to begin 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 AMD who did not progress to NVAMD and those who progressed and received anti-VEGF treatment. Subjects had ≥3 consecutive visits. Two tasks were defined: (I) develop a deep learning (DL) model to identify the need for anti-VEGF treatment, and (II) estimate outer retina thickness (ORT) from Bruch’s membrane (BM) to the outer plexiform layer (OPL) as an imaging biomarker. A convolutional neural network (CNN) extracted OCT features related to ORT, and two long short-term memory (LSTM) models were trained on separate datasets to address Tasks I and II. treatment. 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 AUROC of 0.73 (95% CI, 0.59–0.87), AUPRC (precision-recall curve) of 0.51 (95% CI, 0.35–0.66), an accuracy of 0.87 (95% CI, 0.76–0.97), a precision of 0.73 (95% CI, 0.59–0.87), a sensitivity of 0.69 (95% CI, 0.54–0.83), and a specificity of 0.92 (95% CI, 0.84–1.00). Task II achieved NMAE 0.12 (95% CI, 0.04–0.20). Conclusions This DL framework provides a fully automated approach for determining optimal treatment timing, supporting earlier intervention to preserve visual acuity in NVAMD patients.