2025/09/26 by Agarwal, Anusha, Sarkar, Dibakar Roy, Goswami, Somdatta
#Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Medical Physics (physics.med-ph)
paper · doi:10.48550/arxiv.2510.03248
Background: Traumatic brain injury (TBI) is a major global health concern with 69 million annual cases. While neural operators have revolutionized scientific computing, existing architectures cannot handle the heterogeneous multimodal data (anatomical imaging, scalar demographics, and geometric constraints) required for patient-specific biomechanical modeling. Objective: This study introduces the first multimodal neural operator framework for biomechanics, fusing heterogeneous inputs to predict brain displacement fields for rapid TBI risk assessment. Methods: TBI modeling was reformulated as a multimodal operator learning problem. We proposed two fusion strategies: field projection for Fourier Neural Operator (FNO) architectures and branch decomposition for Deep Operator Networks (DeepONet). Four architectures (FNO, Factorized FNO, Multi-Grid FNO, and DeepONet) were extended with fusion mechanisms and evaluated on 249 in vivo Magnetic Resonance Elastography (MRE) datasets (20-90 Hz). Results: Multi-Grid FNO achieved the highest accuracy (MSE = 0.0023, 94.3% spatial fidelity). DeepONet offered the fastest inference (14.5 iterations/s, 7x speedup), suitable for edge deployment. All architectures reduced computation from hours to milliseconds. Conclusion: Multimodal neural operators enable efficient, real-time, patient-specific TBI risk assessment. This framework establishes a generalizable paradigm for heterogeneous data fusion in scientific domains, including precision medicine.