One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in Ultrasound Images Using Deep Learning

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.