Abstract
Cervical myelopathy (CM) is a pathology of the spinal cord that causes upper limb disorders. CM is often screened by conducting the 10-s grip and release (G&R) test, which mainly focuses on hand dysfunction caused by CM. This test has patients repeat gripping and releasing their hands as quickly as possible. Spine surgeons observe the quickness of this repetition to screen for CM. We propose an automatic screening method of CM that involves patients’ hands recorded as videos when they are performing the G&R test. The videos are used to estimate feature values, i.e., the positions of each part of the hand, which are obtained through image processing. A support vector machine classifier classifies CM patients and controls with these feature values after pre-processing. We validated our method with 10-fold cross-validation and the videos of 20 CM patients and 15 controls. The results indicate that sensitivity, specificity, and area under the receiver operating characteristic curve were \(90.0\%\), \(93.3\%\), and 0.947, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alagha, M.A., Alagha, M.A., Dunstan, E., Sperwer, O., Timmins, K.A., et al.: Development of a new assessment tool for cervical myelopathy using hand-tracking sensor: part 1: validity and reliability. Eur. Spine J. 26(4), 1291–1297 (2017). https://doi.org/10.1007/s00586-017-4948-3
Bandini, A., Green, J.R., Taati, B., Orlandi, S., Zinman, L., et al.: Automatic detection of amyotrophic lateral sclerosis (ALS) from video-based analysis of facial movements: speech and non-speech tasks. In: 2018 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018), pp. 150–157. IEEE, Xi’an (2018). https://doi.org/10.1109/FG.2018.00031
Chanda, P.B., Paul, A., Paul, A., Sarkar, S.: Spondylosis detection and classification of cervical images using ATMFCMC based medical image segmentation methods. In: Proceedings of the International Conference on Advances in Electronics, Electrical and Computational Intelligence (ICAEEC) 2019 (2020). https://doi.org/10.2139/ssrn.3575474
Chang, M., Canseco, J.A., Nicholson, K.J., Patel, N., Vaccaro, A.R.: The role of machine learning in spine surgery: the future is now. Front. Surge. 7(54) (2020). https://doi.org/10.3389/fsurg.2020.00054
Cook, C., Roman, M., Stewart, K.M., Leithe, L.G., Isaacs, R.: Reliability and diagnostic accuracy of clinical special tests for myelopathy in patients seen for cervical dysfunction. J. Orthopaedic Sports Phys. Therapy 39(3), 172–178 (2009). https://doi.org/10.2519/jospt.2009.2938
d’Avanzo, S., Ciavarro, M., Pavone, L., Pasqua, G., Ricciardi, F., et al.: The functional relevance of diffusion tensor imaging in patients with degenerative cervical myelopathy. J. Clin. Med. 9(6) (2020). https://doi.org/10.3390/jcm9061828
Galbusera, F., Casaroli, G., Bassani, T.: Artificial intelligence and machine learning in spine research. JOR Spine 2(1) (2019). https://doi.org/10.1002/jsp2.1044
Gomez, L.F., Morales, A., Orozco-Arroyave, J.R., Daza, R., Fierrez, J.: Improving parkinson detection using dynamic features from evoked expressions in video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 1562–1570. IEEE, Nashville (2021). https://doi.org/10.1109/CVPRW53098.2021.00172
Google: Mediapipe. https://google.github.io/mediapipe/, Accessed 12 July 2021
Harrop, J.S., Naroji, S., Maltenfort, M., Anderson, D.G., Albert, T., et al.: Cervical myelopathy: a clinical and radiographic evaluation and correlation to cervical spondylotic myelopathy. Spine 35(6), 620–624 (2010). https://doi.org/10.1097/BRS.0b013e3181b723af
Hoffman, H., Lee, S.I., Garst, J.H., Lu, D.S., Li, C.H., et al.: Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy. J. Clin. Neurosci 22(9), 1444–1449 (2015). https://doi.org/10.1016/j.jocn.2015.04.002
Hosono, N., Sakaura, H., Mukai, Y., Kaito, T., Makino, T., et al.: A simple performance test for quantifying the severity of cervical myelopathy. J. Bone Joint Surg. Brit. 90(9), 1210–1213 (2008). https://doi.org/10.1302/0301-620X.90B9.20459
Iakovakis, D., Hadjidimitriou, S., Charisis, V., Bostanjopoulou, S., Katsarou, Z., et al.: Early Parkinson’s disease detection via touchscreen typing analysis using convolutional neural networks. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3535–3538. IEEE, Berlin (2019). https://doi.org/10.1109/EMBC.2019.8857211
Khan, O., Badhiwala, J.H., Grasso, G., Fehlings, M.G.: Use of machine learning and artificial intelligence to drive personalized medicine approaches for spine care. World Neurosurg 140, 512–518 (2020). https://doi.org/10.1016/j.wneu.2020.04.022
Machino, M., Ando, K., Kobayashi, K., Morozumi, M., Tanaka, S., et al.: Cut off value in each gender and decade of 10-s grip and release and 10-s step test: A comparative study between 454 patients with cervical spondylotic myelopathy and 818 healthy subjects. Clin. Neurol. Neurosurg. (2019). https://doi.org/10.1016/j.clineuro.2019.105414
Merali, Z., Wang, J.Z., Badhiwala, J.H., Witiw, C.D., Wilson, J.R., et al.: A deep learning model for detection of cervical spinal cord compression in MRI scans. Sci. Rep. 11 (2021). https://doi.org/10.1038/s41598-021-89848-3
Ono, K., Ebara, S., Fuji, T., Yonenobu, K., Fujiwara, K., et al.: Myelopathy hand: new clinical signs of cervical cord damage. J. Bone Joint Surg. Brit. 69(2), 215–219 (1987). https://doi.org/10.1302/0301-620X.69B2.3818752
Sharma, G., Prasad, D., Umapathy, K., Krishnan, S.: Screening and analysis of specific language impairment in young children by analyzing the textures of speech signal. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 964–967. IEEE, Montreal (2020). https://doi.org/10.1109/EMBC44109.2020.9176056
Singh, A., Crockard, H.A.: Quantitative assessment of cervical spondylotic myelopathy by a simple walking test. The Lancet 354(9176), 370–373 (1999). https://doi.org/10.1016/S0140-6736(98)10199-X
Su, X.J., Hou, C.L., Shen, B.D., Zhang, W.Z., Wu, D.S., et al.: Clinical application of a new assessment tool for myelopathy hand using virtual reality. Spine 45(24), E1645–E1652 (2020). https://doi.org/10.1097/BRS.0000000000003696
Watanabe, M., Sugiura, Y., Saito, H., Koyama, T., Fujita, K.: Detection of cervical myelopathy with Leap Motion Sensor by random forests. In: 2020 IEEE 2nd Global Conference on Life Sciences and Technologies (LifeTech), pp. 214–216. IEEE, Kyoto (2020). https://doi.org/10.1109/LifeTech48969.2020.1570620097
Yukawa, Y., Kato, F., Ito, K., Horie, Y., Nakashima, H., et al.: “Ten second step test” as a new quantifiable parameter of cervical myelopathy. Spine 34(1), 82–86 (2009). https://doi.org/10.1097/BRS.0b013e31818e2b19
Acknowledgements
This work was supported by JST PRESTO Grant Number JPMJPR17J4 and JST AIP-PRISM Grant Number JPMJCR18Y2.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Matsui, R., Koyama, T., Fujita, K., Saito, H., Sugiura, Y. (2021). Video-Based Hand Tracking for Screening Cervical Myelopathy. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2021. Lecture Notes in Computer Science(), vol 13018. Springer, Cham. https://doi.org/10.1007/978-3-030-90436-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-90436-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90435-7
Online ISBN: 978-3-030-90436-4
eBook Packages: Computer ScienceComputer Science (R0)