Intelligent evaluation of therapeutic effect of electroacupuncture moxibustion on cerebral ischemia reperfusion injury based on multimodal information fusion and neural network

Abstract

Ischemic stroke and reperfusion injury pose significant challenges in treatment due to their complex pathophysiology and the difficulty of integrating multimodal brain imaging data. Electroacupuncture has shown potential in alleviating reperfusion injury by modulating physiological responses, but assessing its efficacy remains difficult. This study proposes an intelligent evaluation method for electroacupuncture efficacy by integrating multimodal information from CT and MRI images using advanced machine learning techniques. Specifically, a ResNet50-based Convolutional Neural Network (CNN) is employed, enhanced with a Convolutional Block Attention Module (CBAM) and a Multi-Scale Residual Module (MSRM) to improve feature extraction and fusion at multiple scales and multiple modal. The proposed approach effectively captures critical patterns and subtle details across different modalities, improving the accuracy of brain injury and recovery assessments. In experimental evaluations, the method achieved 97.1% accuracy and a 96.1% F1 score, demonstrating the effectiveness of the proposed method.