Deep Learning Algorithm Trained on Cervical Magnetic Resonance Imaging to Predict Outcomes of Transforaminal Epidural Steroid Injection for Radicular Pain from Cervical Foraminal Stenosis

Abstract

Purpose

A convolutional neural network (CNN) is one of the representative deep learning (DL) model that is especially useful for image recognition and classification. In the current study, using cervical axial magnetic resonance imaging (MRI) data obtained prior to transforaminal epidural steroid injection (TFESI), we developed a CNN model to predict the therapeutic outcome of cervical TFESI in patients with cervical foraminal stenosis.

Patients and Methods

We retrospectively recruited 288 patients with cervical foraminal stenosis who received cervical TFESI due to cervical radicular pain. We collected single T2-axial spine MR image obtained from each patient. The image showing narrowest width of the neural foramen in the level at which TFESI was performed was used for input data. A “favor outcome” was defined as a ≥ 50% reduction in the NRS score at 2 months post-TFESI vs the pretreatment NRS score. A “poor outcome” was defined as a < 50% reduction in the NRS score at 2 months post-TFESI vs the pretreatment score.

Results

The area under the curve of our developed model for predicting therapeutic outcome of cervical TFESI in patients with cervical spinal stenosis was 0.801.

Conclusion

We showed that a CNN model trained using cervical axial MRI could be helpful for predicting therapeutic outcome after cervical TFESI in patients with cervical foraminal stenosis.

Keywords:

• convolutional neural network
• artificial intelligence
• deep learning
• spinal stenosis
• cervical spine
• transforaminal epidural steroid injection

Acknowledgments

Ming Xing Wang and Jeoung Kun Kim contributed equally to this work as co-first authors.

Disclosure

The authors report no conflicts of interest in this work.

Additional information

Funding

This study was supported by a National Research Foundation of Korea grant funded by the Korean government (grant no. NRF-2022R1F1A1072553).