PI-MMNet: a cross-modal neural network for predicting neurological deterioration in pontine infarction

Introduction Pontine infarction, a subtype of ischemic stroke, often leads to neurological deterioration (ND). Current diagnostic methods rely mainly on imaging and neglect clinical data, while existing multimodal models struggle with small lesions, heterogeneous inputs, and high computational cost. Methods We propose PI-MMNet, a cross-modal neural network combining: (i) a Multi-modal Feature Processing module with Mamba-based extractors, (ii) a Dynamic Residual Fusion module for robust feature integration, and (iii) an Adaptive Graph module for efficient relational reasoning. A multi-loss strategy jointly optimizes alignment, graph consistency, and classification. Experiments used 386 pontine infarction cases with MRI and clinical data under 5-fold cross-validation. Results PI-MMNet outperformed state-of-the-art methods, improving accuracy by 1.03%, F1 by 0.0504, and AUC by 0.0343, while using only 146 parameters and 135 memory of the strongest baseline. Ablation and visualization confirmed the contributions of all modules. Discussion PI-MMNet provides an efficient and interpretable framework for predicting ND in pontine infarction and may generalize to other multimodal medical tasks. Our code is available at https://github.com/jinhui66/PI-MMNet .