Mild Traumatic Brain Injury (mTBI) is a significant public health challenge due to its high prevalence and potential for long-term health effects. Despite Computed Tomography (CT) being the standard diagnostic tool for mTBI, it often yields normal results in mTBI patients despite symptomatic evidence. This fact underscores the complexity of accurate diagnosis. In this study, we introduce an interpretable 3D Multi-Modal Residual Convolutional Neural Network (MRCNN) for mTBI diagnostic model enhanced with Occlusion Sensitivity Maps (OSM). Our MRCNN model exhibits promising performance in mTBI diagnosis, demonstrating an average accuracy of 82.4%, sensitivity of 82.6%, and specificity of 81.6%, as validated by a five-fold cross-validation process. Notably, in comparison to the CT-based Residual Convolutional Neural Network (RCNN) model, the MRCNN shows an improvement of 4.4% in specificity and 9.0% in accuracy. We show that the OSM offers superior data-driven insights into CT images compared to the Grad-CAM approach. These results highlight the efficacy of the proposed multi-modal model in enhancing the diagnostic precision of mTBI.

翻译：轻度创伤性脑损伤（mTBI）是一项重大的公共卫生挑战，因其高发病率及潜在的长期健康影响。尽管计算机断层扫描（CT）是mTBI的标准诊断工具，但在有症状表现的mTBI患者中，CT结果常显示正常。这一事实凸显了精确诊断的复杂性。本研究提出了一种结合遮挡敏感度图（OSM）的可解释3D多模态残差卷积神经网络（MRCNN），用于构建mTBI诊断模型。经过五折交叉验证，MRCNN模型在mTBI诊断中表现出色，平均准确率达82.4%，敏感性为82.6%，特异性为81.6%。值得注意的是，与基于CT的残差卷积神经网络（RCNN）模型相比，MRCNN在特异性上提高了4.4%，准确率提升了9.0%。研究表明，与Grad-CAM方法相比，OSM能提供更优的数据驱动型CT图像洞察。这些结果突显了所提多模态模型在提升mTBI诊断精度方面的有效性。