Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema

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Diabetic macular edema (DME) is a common complication of diabetes that can lead to vision loss, and anti-vascular endothelial growth factor (anti-VEGF) therapy is the standard of care for DME, but the treatment outcomes vary widely among patients. This study collected optical coherence tomography (OCT) images and clinical data from DME patients who received anti-VEGF treatment to develop and validate deep learning (DL) models for predicting the anti-VEGF outcomes in DME patients based on convolutional neural network (CNN) and multilayer perceptron (MLP) combined architecture by using multimodal data. An Xception-MLP architecture was utilized to predict best-corrected visual acuity (BCVA), central subfield thickness (CST), cube volume (CV), and cube average thickness (CAT). Mean absolute error (MAE), mean squared error (MSE) and mean squared logarithmic error (MSLE) were employed to evaluate the model performance. In this study, both the training set and the validation set exhibited a consistent decreasing trend in MAE, MSE, and MSLE. No statistical difference was found between the actual and predicted values in all clinical indicators. This study demonstrated that the improved CNN-MLP regression models using multimodal data can accurately predict outcomes in BCVA, CST, CV, and CAT after anti-VEGF therapy in DME patients, which is valuable for ophthalmic clinical decisions and reduces the economic burden on patients.