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Abstract：
This study investigated the usefulness of deep learning-based automatic detection of temporomandibular joint (TMJ) effusion using magnetic resonance imaging (MRI) in patients with temporomandibular disorder and whether the diagnostic accuracy of the model improved when patients\' clinical information was provided in addition to MRI images. The sagittal MR images of 2948 TMJs were collected from 1017 women and 457 men (mean age 37.19 ± 18.64 years). The TMJ effusion diagnostic performances of three convolutional neural networks (scratch, fine-tuning, and freeze schemes) were compared with those of human experts based on areas under the curve (AUCs) and diagnosis accuracies. The fine-tuning model with proton density (PD) images showed acceptable prediction performance (AUC = 0.7895), and the from-scratch (0.6193) and freeze (0.6149) models showed lower performances (p < 0.05). The fine-tuning model had excellent specificity compared to the human experts (87.25% vs. 58.17%). However, the human experts were superior in sensitivity (80.00% vs. 57.43%) (all p < 0.001). In gradient-weighted class activation mapping (Grad-CAM) visualizations, the fine-tuning scheme focused more on effusion than on other structures of the TMJ, and the sparsity was higher than that of the from-scratch scheme (82.40% vs. 49.83%, p < 0.05). The Grad-CAM visualizations agreed with the model learned through important features in the TMJ area, particularly around the articular disc. Two fine-tuning models on PD and T2-weighted images showed that the diagnostic performance did not improve compared with using PD alone (p < 0.05). Diverse AUCs were observed across each group when the patients were divided according to age (0.7083-0.8375) and sex (male:0.7576, female:0.7083). The prediction accuracy of the ensemble model was higher than that of the human experts when all the data were used (74.21% vs. 67.71%, p < 0.05). A deep neural network (DNN) was developed to process multimodal data, including MRI and patient clinical data. Analysis of four age groups with the DNN model showed that the 41-60 age group had the best performance (AUC = 0.8258). The fine-tuning model and DNN were optimal for judging TMJ effusion and may be used to prevent true negative cases and aid in human diagnostic performance. Assistive automated diagnostic methods have the potential to increase clinicians\' diagnostic accuracy.
摘要：
这项研究调查了使用磁共振成像（MRI）对颞下颌关节（TMJ）积液进行基于深度学习的自动检测的实用性，以及在提供患者临床信息时模型的诊断准确性是否提高MRI图像。从1017名女性和457名男性（平均年龄37.19±18.64岁）收集了2948名TMJ的矢状MR图像。三个卷积神经网络的TMJ积液诊断性能(划痕，微调，和冻结方案）根据曲线下面积（AUC）和诊断准确性与人类专家进行了比较。具有质子密度（PD）图像的微调模型显示出可接受的预测性能（AUC=0.7895），从零开始(0.6193)和冷冻(0.6149)模型表现出较低的性能(p<0.05)。与人类专家相比，微调模型具有出色的特异性（87.25％vs.58.17%）。然而,人类专家的灵敏度更高(80.00%vs.57.43%)(所有p<0.001)。在梯度加权类激活映射(Grad-CAM)可视化中，微调方案更侧重于渗出性，而不是TMJ的其他结构，稀疏度高于从头开始方案(82.40%vs.49.83%,p<0.05)。Grad-CAM可视化与通过TMJ区域的重要特征学习的模型一致，特别是在关节盘周围。PD和T2加权图像上的两个微调模型表明，与单独使用PD相比，诊断性能没有改善（p<0.05）。当根据年龄(0.7083-0.8375)和性别(男性:0.7576,女性:0.7083)对患者进行分组时,在每个组中观察到不同的AUC。当使用所有数据时，集成模型的预测精度高于人类专家的预测精度(74.21%vs.67.71%,p<0.05)。开发了深度神经网络(DNN)来处理多模态数据，包括MRI和患者临床资料。用DNN模型对四个年龄组的分析显示，41-60岁年龄组的表现最好（AUC=0.8258）。微调模型和DNN是判断TMJ积液的最佳选择，可用于防止真正的阴性病例并帮助人类诊断性能。辅助自动诊断方法有可能提高临床医生的诊断准确性。
