EMBC2023

1. Yukina Sato1), Kana Matsuo1), Yohei Kawasaki1), Takafumi Koyama2), Eriku Yamada2), Koji Fujita2), and Yuta Sugiura1)
1) Keio University 2) Tokyo Medical and Dental University
01 Introduction
02 Methods
Record ultrasound videos
• Numbness of the fingers
• Compression of the median nerve
• The most common upper extremity
neuropathy
Carpal Tunnel Syndrome (CTS)
• Detect CTS using deep learning-based
analyses of ultrasound images
• Automatic tracking of the median nerve
in ultrasound videos
Related work
Clarifying whether using ultrasound
video improves accuracy of CTS
estimation
Purpose
03 Results
04 Discussions
• Improve accuracy
• Take into account the time variation in median nerve tracking
• Obtain features for each part of the same finger movement
• Use a larger dataset
05 Conclusion
• Segmented the median nerve in ultrasound videos
• Performed CTS estimations
• Estimations using features obtained from videos and time
series data showed higher accuracy than those using
features obtained from images.
E-mail： info-lcl-group@keio.jp
Carpal Tunnel Syndrome Estimation
through Median Nerve Segmentation
in Ultrasound Videos
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Features from images Features from videos Time series
Sensitivity
Specificity
Features from
images
Features from
videos
Time
series
Sensitivity 0.842 0.855 0.947
Specificity 0.581 0.677 0.645
Features
Frame 1 second
after the start of
recording
Median and
maximum of
all frames
All frames
Model XGBoost XGBoost KNN
Comparison of the results of the classification
Accuracy Comparison
Segmentation Feature extraction Classification
134 cases taken by three skilled physicians Segment the nerve area
by Mask R-CNN
Obtain the features by image
processing
• Area
• Perimeter
• Aspect ratio
• Centroid coordinates etc.
Group 63-fold cross-
validation