Objective: Ultrasound (US) examination has unique advantages in diagnosing carpal tunnel syndrome (CTS) while identifying the median nerve (MN) and diagnosing CTS depends heavily on the expertise of examiners. To alleviate this problem, we aimed to develop a one-stop automated CTS diagnosis system (OSA-CTSD) and evaluate its effectiveness as a computer-aided diagnostic tool. Methods: We combined real-time MN delineation, accurate biometric measurements, and explainable CTS diagnosis into a unified framework, called OSA-CTSD. We collected a total of 32,301 static images from US videos of 90 normal wrists and 40 CTS wrists for evaluation using a simplified scanning protocol. Results: The proposed model showed better segmentation and measurement performance than competing methods, reporting that HD95 score of 7.21px, ASSD score of 2.64px, Dice score of 85.78%, and IoU score of 76.00%, respectively. In the reader study, it demonstrated comparable performance with the average performance of the experienced in classifying the CTS, while outperformed that of the inexperienced radiologists in terms of classification metrics (e.g., accuracy score of 3.59% higher and F1 score of 5.85% higher). Conclusion: The OSA-CTSD demonstrated promising diagnostic performance with the advantages of real-time, automation, and clinical interpretability. The application of such a tool can not only reduce reliance on the expertise of examiners, but also can help to promote the future standardization of the CTS diagnosis process, benefiting both patients and radiologists.

通过开发和评估一种一站式自动化诊断系统（OSA-CTSD），本研究旨在缓解对检查员专业知识的依赖问题，通过实时中位神经勾画、准确生物测量和可解释的诊断集成框架，实现对腕管综合征（CTS）的一站式自动化诊断，展示了良好的分割和测量性能，并在读者研究中表现出与经验丰富的分类表现相媲美的性能，同时在分类指标方面优于经验不足的放射科医师，具有实时、自动化和临床可解释性等优点，既可以减少对检查员专业知识的依赖，也有助于促进CTS诊断过程的未来标准化，使得患者和放射科医师受益。

使用深度学习的超声图像自动化诊断系统一站式对腕管综合症的应用