Mild Traumatic Brain Injury (mTBI) is a common and challenging condition to diagnose accurately. Timely and precise diagnosis is essential for effective treatment and improved patient outcomes. Traditional diagnostic methods for mTBI often have limitations in terms of accuracy and sensitivity. In this study, we introduce an innovative approach to enhance mTBI diagnosis using 3D Computed Tomography (CT) images and a metric learning technique trained with triplet loss. To address these challenges, we propose a Residual Triplet Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases and healthy ones by embedding 3D CT scans into a feature space. The triplet loss function maximizes the margin between similar and dissimilar image pairs, optimizing feature representations. This facilitates better context placement of individual cases, aids informed decision-making, and has the potential to improve patient outcomes. Our RTCNN model shows promising performance in mTBI diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and a specificity of 95.2%, as confirmed through a five-fold cross-validation. Importantly, when compared to the conventional Residual Convolutional Neural Network (RCNN) model, the RTCNN exhibits a significant improvement, showcasing a remarkable 22.5% increase in specificity, a notable 16.2% boost in accuracy, and an 11.3% enhancement in sensitivity. Moreover, RTCNN requires lower memory resources, making it not only highly effective but also resource-efficient in minimizing false positives while maximizing its diagnostic accuracy in distinguishing normal CT scans from mTBI cases. The quantitative performance metrics provided and utilization of occlusion sensitivity maps to visually explain the model's decision-making process further enhance the interpretability and transparency of our approach.

翻译：轻度创伤性脑损伤是一种常见且难以准确诊断的疾病。及时精准的诊断对有效治疗和改善患者预后至关重要。传统诊断方法在准确性和敏感性方面常存在局限性。本研究提出一种创新方法，通过三维计算机断层扫描（CT）图像和基于三元组损失训练的度量学习技术提升mTBI诊断效能。为应对上述挑战，我们提出残差三元组卷积神经网络（RTCNN）模型，通过将三维CT扫描嵌入特征空间来区分mTBI病例与健康对照。三元组损失函数通过最大化相似与不相似图像对之间的间隔，优化特征表征能力，从而更精准地定位个体病例的临床语境，辅助临床决策，并具有改善患者预后的潜力。RTCNN模型在mTBI诊断中展现出优异性能：经五折交叉验证，平均准确率达94.3%，敏感性94.1%，特异性95.2%。与常规残差卷积神经网络（RCNN）模型相比，RTCNN在特异性上显著提升22.5%，准确率提高16.2%，敏感性增强11.3%。更值得注意的是，RTCNN所需内存资源更低，在最大化区分正常CT与mTBI病例的诊断准确性的同时，兼具高效性与资源节约性，有效降低假阳性率。本研究通过量化性能指标及利用遮挡敏感性图可视化模型决策过程，进一步提升了方法的可解释性与透明度。