Patients who undergo surgery for cervical radiculopathy are at risk of developing adjacent segment disease (ASD) and in this study a deep learning algorithm was developed to predict ASD using only the preoperative cervical MRI of patients undergoing single-level anterior cervical discectomy and fusion (ACDF).
Retrospective chart review was performed and after applying in- and exclusion criteria 344 patients were included of which 60% (n = 208) was used for training and 40% for validation (n = 43) and testing (n = 93). A deep learning-based prediction model was trained using preoperative T2-sagittal cervical MRI.
The model was able to predict ASD on the 93 test images with an accuracy of 88 / 93 [95%, CI 90.0 – 99.2], sensitivity 12 / 15 [80%, CI 59.8 – 100] and specificity 76 / 78 [97%, CI 93.9 – 100]. The model outperformed the neuroradiologist and neurosurgeon in all outcome metrics and McNemar’s test demonstrated that the proportion of wrongful predictions was significantly lower by the model (Test Statistic 2.000, p-value < 0.001).
A deep learning algorithm using only preoperative cervical T2 MRI outperformed a clinical expert at predicting new symptoms at adjacent levels.