TITLE:
Multi-Task 3D Neuroimaging Model for Simultaneous Prediction of Medication Response and Disease Severity: A Synthetic Data Study
AUTHORS:
Rocco de Filippis, Abdullah Al Foysal
KEYWORDS:
Neuroimaging, Multi-Task Learning, 3D Convolutional Neural Network, Medication Response Prediction, Disease Severity Estimation, Synthetic Data Generation, Deep Learning, Personalized Medicine, Focal Loss, Multi-Modal Analysis
JOURNAL NAME:
Open Access Library Journal,
Vol.12 No.8,
August
25,
2025
ABSTRACT: Accurately predicting medication response and disease severity is essential for advancing personalized treatment strategies, especially in complex neuropsychiatric conditions. In this study, we propose a novel multi-task deep learning framework capable of simultaneously predicting both medication response classes and disease severity scores from neuroimaging data. To support this, we generated a high-contrast synthetic 3D neuroimaging dataset comprising 1500 samples, simulating distinct anatomical changes in critical brain regions such as the prefrontal cortex and hippocampus, which were systematically linked to varying severity levels and treatment response categories. The proposed model employs a 3D dual-path convolutional neural network with a shared encoder and two separate branches for regression and classification tasks. The architecture is optimized with batch normalization, dropout, focal loss for class imbalance, and gradient clipping for training stability. Training was performed using the Adam optimizer with adaptive learning rate reduction and early stopping to ensure optimal performance and prevent overfitting. The model achieved exceptional results, with 100% classification accuracy for medication response prediction and a Mean Absolute Error (MAE) of 8.03 in severity estimation on the test set. Performance evaluation using confusion matrices, regression scatter plots, and error distribution analyses confirmed the robustness and reliability of the model. The multi-task setup effectively leveraged shared representations, improving learning efficiency and predictive power. This research demonstrates the feasibility and potential of multi-task learning in neuroimaging applications, providing a promising step toward integrating both categorical and continuous clinical outcomes in a unified predictive framework. Future work will focus on validation using real-world clinical datasets and expanding the architecture to accommodate multimodal patient data.