Machine Learning for the Prediction of Cervical Spondylotic Myelopathy: A Post Hoc Pilot Study of 28 Participants

Background: Cervical spondylotic myelopathy (CSM) severity and presence of symptoms are often difficult to predict based simply on clinical imaging alone. Similarly, improved machine learning techniques provide new tools with immense clinical potential. Methods: A total of 14 patients with CSM and 14 controls underwent imaging of the cervical spine. Two different artificial neural network models were trained; 1) to predict CSM diagnosis; and 2) to predict CSM severity. Model 1 consisted of 6 inputs including 3 common imaging scales for the evaluation of cord compression, alongside 3 objective magnetic resonance imaging measurements. The outcome for model 1 was binary to predict CSM diagnosis. Model 2 consisted of 23 input variables derived from probabilistic volume mapping measurements of white matter tracts in the region of compression. The outcome of model 2 was linear, to predict the modified Japanese Orthopedic Association (mJOA) score. Results: Model 1 was used in predicting CSM. The mean cross-validated accuracy of the trained model was 86.50% (95% confidence interval, 85.16%–87.83%) with a median accuracy of 90.00%. Area under the curve (AUC) was calculated for each repetition. Average AUC for each repetition was 0.947 with a median AUC of 1.0. Average sensitivity, specificity, positive predictive value, and negative predictive value were 90.25%, 85.05%, 81.58%, and 91.94%, respectively. Model 2 was used in modeling mJOA. The mJOA model predicted scores, with a mean and median error of –0.29 mJOA points and –0.08 mJOA points, respectively, mean error per batch was 0.714 mJOA points. Conclusions: Machine learning provides a promising method for prediction, diagnosis, and even prognosis in patients with CSM.