Multimodal deep learning prediction of treatment response to anti-vascular endothelial growth factor in diabetic macular oedema.

objectiveTo develop and validate a multimodal deep learning model that predicts treatment responses to intravitreal anti-vascular endothelial growth factor (anti-VEGF) injections in patients with diabetic macular oedema (DMO) by combining optical coherence tomography images and clinical data.

methodsThis study included 107 DMO patients who received three consecutive anti-VEGF treatments. Model performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, and specificity. The model's predictions were compared with those of retinal specialists.

resultsAmong 107 patients, 65 showed good response and 42 showed poor response to treatment. The multimodal model achieved an AUROC of 0.962 (95% CI, 0.945-0.979), accuracy of 0.953 (95% CI, 0.933-0.973), sensitivity of 0.969 (95% CI, 0.951-0.987), and specificity of 0.928 (95% CI, 0.903-0.953) in the internal validation. The model outperformed retinal specialists, who achieved accuracies ranging from 0.571 to 0.857.

conclusionThe multimodal deep learning model demonstrated high accuracy in predicting anti-VEGF treatment responses in DMO patients. This approach could enable more personalised treatment strategies and optimal resource utilisation in ophthalmological care. Further validation with larger, multicentre datasets is warranted to confirm its clinical utility.