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.
Degenerative Cervical Myelopathy (DCM) is the functional derangement of the spinal cord and acts as one of the most common atraumatic spinal cord injuries. Magnetic resonance imaging (MRI) are key in confirming the diagnosis of DCM in patients, though the utilization of higher fidelity magnetic resonance imaging scans and their integration into machine learning models remains largely unexplored. This study looks at the predictive ability of common community MRI scans in comparison to high fidelity scans in disease diagnosis. We hypothesize that the utilization of higher fidelity "advanced" MRI scans will increase the effectiveness of machine learning models predicting DCM severity. Through the utilization of Random Forest Classifiers, we have been able to predict disease severity with 41.8% accuracy in current community MRI scans and 63.9% in the advanced MRI scans. Furthermore, across the different predictive model variations tested, the advanced MRI scans consistently produced higher prediction accuracies compared to the community MRI counterparts. These results support our hypothesis and indicate that machine learning models have the potential to predict disease severity. However, neither performed well enough to be considered for use in clinical practice, indicating that the utilization of more sophisticated machine models may be required for these purposes.
With the common adoption of electronic health records and new technologies capable of producing an unprecedented scale of data, a shift must occur in how we practice medicine in order to utilize these resources. We are entering an era in which the capacity of even the most clever human doctor simply is insufficient. As such, realizing “personalized” or “precision” medicine requires new methods that can leverage the massive amounts of data now available. Machine learning techniques provide one important toolkit in this venture, as they are fundamentally designed to deal with (and, in fact, benefit from) massive datasets. The clinical applications for such machine learning systems are still in their infancy, however, and the field of medicine presents a unique set of design considerations. In this chapter, we will walk through how we selected and adjusted the “Progressive Learning framework” to account for these considerations in the case of Degenerative Cervical Myeolopathy. We additionally compare a model designed with these techniques to similar static models run in “perfect world” scenarios (free of the clinical issues address), and we use simulated clinical data acquisition scenarios to demonstrate the advantages of our machine learning approach in providing personalized diagnoses.
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