One-Stop Automated Diagnostic System for Carpal Tunnel Syndrome in
  Ultrasound Images Using Deep Learning
Jiayu Peng Jiajun Zeng Manlin Lai Ruobing Huang Dong Ni Zhenzhou Li
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.