基于深度学习的手腕管综合征超声图像一站式自动化诊断系统
One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning
- The Second People’s Hospital of Shenzhen（深圳市第二人民医院）
- The First Affiliated Hospital of Shenzhen University（深圳大学第一附属医院）
- Shenzhen University Health Science Center（深圳大学医学部）
- Shenzhen University（深圳大学）
- The University of Hong Kong-Shenzhen Hospital（香港大学深圳医院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出OSA-CTSD，一个结合实时正中神经勾画、生物测量和可解释诊断的深度学习系统，用于超声图像中腕管综合征的一站式自动化诊断，性能优于竞争方法并接近经验丰富的阅片者。
AI中文摘要：
目的：超声（US）检查在诊断腕管综合征（CTS）方面具有独特优势，而识别正中神经（MN）和诊断CTS在很大程度上依赖于检查者的专业知识。为缓解这一问题，我们旨在开发一种一站式自动化CTS诊断系统（OSA-CTSD），并评估其作为计算机辅助诊断工具的有效性。方法：我们将实时正中神经勾画、精确的生物特征测量和可解释的CTS诊断整合到一个统一框架中，称为OSA-CTSD。我们采用简化扫描方案，从90个正常手腕和40个CTS手腕的超声视频中收集了总计32,301张静态图像用于评估。结果：所提出的模型在分割和测量性能上优于竞争方法，报告显示HD95评分为7.21像素，ASSD评分为2.64像素，Dice评分为85.78%，IoU评分为76.00%。在阅片者研究中，该模型在CTS分类方面表现出与经验丰富的阅片者平均性能相当的水平，同时在分类指标上优于经验不足的放射科医生（例如，准确率得分高出3.59%，F1得分高出5.85%）。结论：OSA-CTSD展示了有前景的诊断性能，具有实时性、自动化和临床可解释性的优势。此类工具的应用不仅能减少对检查者专业知识的依赖，还有助于推动未来CTS诊断流程的标准化，惠及患者和放射科医生。
英文摘要：
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.
