Role of MRI in Cervical Spondylotic Myelopathy with Other Pathological Findings: Case Report and Literature Review

Deep learning algorithm to evaluate cervical spondylotic myelopathy using lateral cervical spine radiograph

BACKGROUND

Deep learning (DL) is an advanced machine learning approach used in different areas such as image analysis, bioinformatics, and natural language processing. A convolutional neural network (CNN) is a representative DL model that is highly advantageous for imaging recognition and classification This study aimed to develop a CNN using lateral cervical spine radiograph to detect cervical spondylotic myelopathy (CSM).

METHODS

We retrospectively recruited 207 patients who visited the spine center of a university hospital. Of them, 96 had CSM (CSM patients) while 111 did not have CSM (non-CSM patients). CNN algorithm was used to detect cervical spondylotic myelopathy. Of the included patients, 70% (145 images) were assigned randomly to the training set, while the remaining 30% (62 images) to the test set to measure the model performance.

RESULTS

The accuracy of detecting CSM was 87.1%, and the area under the curve was 0.864 (95% CI, 0.780-0.949).

CONCLUSION

The CNN model using the lateral cervical spine radiographs of each patient could be helpful in the diagnosis of CSM.