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.

使用三维计算机断层扫描图像和基于度量学习技术的残余三元组卷积神经网络模型提高轻度创伤性脑损伤（mTBI）的诊断，通过嵌入特征空间将 CT 扫描者区分为 mTBI 病例和正常者，并实现 94.3% 的平均准确率、94.1% 的灵敏度和 95.2% 的特异度，并相较于传统的残余卷积神经网络模型，在诊断准确性上有显著提升，同时需要更少的内存资源，提高了资源利用率。

使用 3D CT 的剩余三元卷积神经网络增强轻度脑损伤诊断

Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural  Network Using 3D CT

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.

通过在 Mild Traumatic Brain Injury (mTBI) 诊断模型中增加解释性的 3D 多模态残差卷积神经网络（MRCNN）和屏蔽敏感图（OSM），我们展示了该模型在 mTBI 诊断中的出色表现，平均准确度为 82.4%，敏感度为 82.6%，特异度为 81.6%，并经过五折交叉验证过程验证。与基于 CT 的残差卷积神经网络（RCNN）模型相比，MRCNN 在特异度方面提高了 4.4%，在准确度方面提高了 9.0%。研究结果表明，OSM 相比 Grad-CAM 方法，提供了更好的 CT 图像的数据驱动洞察力，凸显了该多模态模型在提高 mTBI 诊断精确度方面的功效。