Table 1.

Summary of literature review of machine learning in outcome prediction after SCI

Study	Description
A ML approach for specification of spinal cord injuries using fractional anisotropy values obtained from diffusion tensor images [27].	Developed KNN and SVM models to predict the presence of spinal cord injury in individual axial slices of the spinal cord collected from DTI, specifically the fractional anisotropy parameter.
Convolutional neural network-based automated segmentation of the spinal cord and contusion injury: deep learning biomarker correlates of motor impairment in acute spinal cord injury [29].	Developed a convolutional neural network to perform segmentation of the spinal cord in tSCI. Segmentation helped authors conclude that contusion injury volume was significantly correlated with motor scores at admission and discharge.
Development of an unsupervised ML algorithm for the prognostication of walking ability in spinal cord injury patients [31].	Constructed unsupervised ML algorithm predicting independent ambulation ability post-SCI at discharge or at the 1-year follow-up. Compared ML algorithm to logistic regression model – no significant difference found in performance.
Use of multivariate linear regression and support vector regression to predict functional outcome after surgery for cervical spondylotic myelopathy.	Compared a support vector regression model with a multivariate logistic regression model in the prediction of functional outcome after surgery for DCM. Support vector regression model was found to be superior.
Using a ML approach to predict outcome after surgery for degenerative cervical myelopathy [15].	Formulated random forest predicting quality-of-life and functional outcomes after decompression surgery for DCM (AUC = 0.70).
ML for prediction of sustained opioid prescription after anterior cervical discectomy and fusion (ACDF).	Developed stochastic gradient boosting model (AUC = 0.81) to predict sustained opioid prescription after ACDF. Major predictors of lengthened opioid prescription included preoperative opioid prescription, antidepressant use, tobacco use, and Medicaid insurance status.
Prognosis of cervical myelopathy based on diffusion tensor imaging with artificial intelligence methods.	Utilized multiple supervised learning models (e.g., SVM) that used DTI features to predict the mJOA recovery rate at the 1-year postsurgery follow-up.
Development of ML algorithms for prediction of prolonged opioid prescription after surgery for lumbar disc herniation	Created an elastic-net penalized logistic regression model (AUC = 0.81) to predict sustained opioid prescription after lumbar disc herniation surgery. Major predictors of lengthened opioid prescription included instrumentation, preoperative opioid duration, and comorbid depression.
Development of ML algorithms for prediction of discharge disposition after elective inpatient surgery for lumbar degenerative disc disorders	Created a neural network (AUC = 0.82) to predict nonroutine (i.e., not home) discharge for patients undergoing surgery for lumbar degenerative disc disease based on age, comorbid status, etc.

SCI, spinal cord injury; KNN, k-nearest neighbor; SVM, support vector machine; DTI, diffusion tensor imaging; tSCI, traumatic SCI; ML, machine learning; DCM, degenerative cervical myelopathy; AUC, area under the curve; mJOA, modified Japanese Orthopaedic Association.