Severity Assessment of Cervical Spondylotic Myelopathy Based on Intelligent Video Analysis

Cervical spondylotic myelopathy (CSM) has a high incidence in the middle-aged and elderly people. According to clinical research, there is a connection between hand dexterity and cervical nerves. So the surgeon makes a preliminary assessment of the severity of CSM based on a 10-second grip and relea...

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Bibliographic Details
Published in:IEEE journal of biomedical and health informatics 2022-09, Vol.26 (9), p.4486-4496
Main Authors: Zheng, Shuhao, Liang, Guoyan, Chen, Junying, Duan, Qifei, Chang, Yunbing
Format: Article
Language:English
Subjects:
10-Second G&R test
Analytical models
Artificial intelligence
Artificial neural networks
Cameras
Central nervous system diseases
cervical spondylotic myelopathy
Data models
Decision making
hand motion analysis
Inference
Lighting
Motion segmentation
Nerves
Neural networks
Smart phones
Spinal cord
Statistical analysis
Streaming media
Surgeons
Three dimensional models
Video
video analysis
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Summary:Cervical spondylotic myelopathy (CSM) has a high incidence in the middle-aged and elderly people. According to clinical research, there is a connection between hand dexterity and cervical nerves. So the surgeon makes a preliminary assessment of the severity of CSM based on a 10-second grip and release (G&R) test. At present, the statistics of G&R test rely on the surgeon's manual counting. When a patient's hand motion speed is too fast, the surgeon's manual counting is prone to error, leading to potential misdiagnosis. On the other hand, in recent years, artificial intelligence has been developed rapidly, where three-dimensional convolutional neural networks (3D-CNNs) have been widely used in video analysis. This work proposes a hand motion analysis model using a 3D-CNN combined with a de-jittering mechanism to assess the severity of CSM on 10-second G&R videos. We collect 1500 10-second G&R videos recorded by 750 subjects to establish a dataset. The proposed model using 3D-MobileNetV2 as the classifier obtains a Levenshtein accuracy of 97.40% and an average GPU inference time of 3.31 seconds for each 10-second G&R video. Such accuracy and inference speed ensure that the proposed model can be used as a screening examination tool for CSM and a medical assistance tool to help decision making during CSM treatment planning.
ISSN:2168-2194
2168-2208
DOI:10.1109/JBHI.2022.3184870