Machine Learning-based Tools for Predicting Neurological Deterioration in Non-operative Degenerative Cervical Myelopathy Patients

Al-Shawwa, Abdul-Jawwad

Machine Learning-based Tools for Predicting Neurological Deterioration in Non-operative Degenerative Cervical Myelopathy Patients

dc.contributor.advisor	Cadotte, David W.
dc.contributor.author	Al-Shawwa, Abdul-Jawwad
dc.contributor.committeemember	Anderson, David W.
dc.contributor.committeemember	Casha, Steven
dc.contributor.committeemember	Dukelow, Sean P.
dc.date.accessioned	2024-06-18T16:27:37Z
dc.date.available	2024-06-18T16:27:37Z
dc.date.issued	2024-06-27
dc.description.abstract	Background Degenerative cervical myelopathy (DCM) is the most common form of atraumatic spinal cord injury globally. DCM is characterized by the progressive compression of the cervical spinal cord as a result of vertebral column spondylotic degeneration. While surgery is currently the only effective treatment of DCM, clinical guidelines remain unclear on the benefit of surgery for patients with mild forms of DCM. This thesis utilizes machine learning based tools to elucidate clinical and imaging indicators of neurological deterioration in non-operative DCM patients. This thesis is comprised of two independent studies, one focussing on clustering patient groups at risk of deteriorating and the second focusing on developing a supervised machine learning (ML) model capable of predicting neurological deterioration. Methods DCM patients recruited from 2016-2023 underwent MRI scans, including T2w, diffusion tensor imaging (DTI), and magnetization transfer (MT) scans, along with a series of clinical metrics. These were collected every six months, resulting in 124 overall entries. T2w imaging scans were assessed for spinal cord compression, and cervical spinal canal diameter (SCD) was measured. Clustering was achieved through PaCMAP dimensionality reduction and K-Means clustering for the first study. Logistic regressions, support vector classifiers, and random forest classifiers were trained and tested for the second study. Findings We elucidated five patient groups with their associated risks of deterioration, according to both SCD range and cord compression pattern. Furthermore, we found that the compression pattern is unimportant at SCD extremes (≤14.5 mm or >15.75mm). Our best-performing supervised ML model had a testing balanced accuracy of 0.830 and ROC-AUC of 0.87. The three most important metrics for predicting neurological deterioration based on the model were MT ratio above the maximally compressed cervical level in the dorsal and ventral funiculi, and moderate tingling in the arm, shoulder, or hand (quickDASH item 10). Significance and Conclusion SCD and focal cord compression alone do not reliably predict an increased risk of neurological deterioration, their combination does. Furthermore, MT and DTI scans improve the prediction of neurological deterioration in non-operative mild DCM patients.
dc.identifier.citation	Al-Shawwa, A. -J. (2024). Machine learning-based tools for predicting neurological deterioration in non-operative degenerative cervical myelopathy patients (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca.
dc.identifier.uri	https://hdl.handle.net/1880/118974
dc.identifier.uri	https://doi.org/10.11575/PRISM/46570
dc.language.iso	en
dc.publisher.faculty	Graduate Studies
dc.publisher.institution	University of Calgary
dc.rights	University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subject	Machine Learning
dc.subject	Spinal Cord Injury
dc.subject	Degenerative Cervical Myelopathy
dc.subject.classification	Artificial Intelligence
dc.subject.classification	Bioinformatics
dc.subject.classification	Biophysics--Medical
dc.title	Machine Learning-based Tools for Predicting Neurological Deterioration in Non-operative Degenerative Cervical Myelopathy Patients
dc.type	master thesis
thesis.degree.discipline	Medicine – Medical Sciences
thesis.degree.grantor	University of Calgary
thesis.degree.name	Master of Science (MSc)
ucalgary.thesis.accesssetbystudent	I do not require a thesis withhold – my thesis will have open access and can be viewed and downloaded publicly as soon as possible.