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Article

Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools

1
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Department of Clinical Neurosciences, Section of Neurosurgery, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
3
Combined Orthopedic and Neurosurgery Spine Program, University of Calgary, Calgary, AB T2N 1N4, Canada
4
Section of Orthopaedic Surgery, Department of Surgery, University of Calgary, Calgary, AB T2N 1N4, Canada
5
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montrèal, Montrèal, QC H3T 1J4, Canada
6
Functional Neuroimaging Unit, CRIUGM, Universitè de Montrèal, Montrèal, QC H3T 1J4, Canada
7
Mila-Quebec AI Institute, Montrèal, QC T2N 1N4, Canada
8
Department of Biochemistry and Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Allan R. Martin
J. Clin. Med. 2021, 10(4), 892; https://doi.org/10.3390/jcm10040892
Received: 30 January 2021 / Revised: 16 February 2021 / Accepted: 18 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Management of Degenerative Cervical Myelopathy and Spinal Cord Injury)
Despite Degenerative Cervical Myelopathy (DCM) being the most common form of spinal cord injury, effective methods to evaluate patients for its presence and severity are only starting to appear. Evaluation of patient images, while fast, is often unreliable; the pathology of DCM is complex, and clinicians often have difficulty predicting patient prognosis. Automated tools, such as the Spinal Cord Toolbox (SCT), show promise, but remain in the early stages of development. To evaluate the current state of an SCT automated process, we applied it to MR imaging records from 328 DCM patients, using the modified Japanese Orthopedic Associate scale as a measure of DCM severity. We found that the metrics extracted from these automated methods are insufficient to reliably predict disease severity. Such automated processes showed potential, however, by highlighting trends and barriers which future analyses could, with time, overcome. This, paired with findings from other studies with similar processes, suggests that additional non-imaging metrics could be added to achieve diagnostically relevant predictions. Although modeling techniques such as these are still in their infancy, future models of DCM severity could greatly improve automated clinical diagnosis, communications with patients, and patient outcomes. View Full-Text
Keywords: degenerative cervical myelopathy; personalized medicine; machine learning; spinal cord degenerative cervical myelopathy; personalized medicine; machine learning; spinal cord
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MDPI and ACS Style

Ost, K.; Jacobs, W.B.; Evaniew, N.; Cohen-Adad, J.; Anderson, D.; Cadotte, D.W. Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools. J. Clin. Med. 2021, 10, 892. https://doi.org/10.3390/jcm10040892

AMA Style

Ost K, Jacobs WB, Evaniew N, Cohen-Adad J, Anderson D, Cadotte DW. Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools. Journal of Clinical Medicine. 2021; 10(4):892. https://doi.org/10.3390/jcm10040892

Chicago/Turabian Style

Ost, Kalum, W. B. Jacobs, Nathan Evaniew, Julien Cohen-Adad, David Anderson, and David W. Cadotte. 2021. "Spinal Cord Morphology in Degenerative Cervical Myelopathy Patients; Assessing Key Morphological Characteristics Using Machine Vision Tools" Journal of Clinical Medicine 10, no. 4: 892. https://doi.org/10.3390/jcm10040892

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