Assessment of Fusion After Anterior Cervical Discectomy and Fusion Using Convolutional Neural Network Algorithm

Detection of Cervical Foraminal Stenosis from Oblique Radiograph Using Convolutional Neural Network Algorithm

PURPOSE

This study was conducted to develop a convolutional neural network (CNN) algorithm that can diagnose cervical foraminal stenosis using oblique radiographs and evaluate its accuracy.

MATERIALS AND METHODS

A total of 997 patients who underwent cervical MRI and cervical oblique radiographs within a 3-month interval were included. Oblique radiographs were labeled as "foraminal stenosis" or "no foraminal stenosis" according to whether foraminal stenosis was present in the C2-T1 levels based on MRI evaluation as ground truth. The CNN model involved data augmentation, image preprocessing, and transfer learning using DenseNet161. Visualization of the location of the CNN model was performed using gradient-weight class activation mapping (Grad-CAM).

RESULTS

The area under the curve (AUC) of the receiver operating characteristic curve based on DenseNet161 was 0.889 (95% confidence interval, 0.851-0.927). The F1 score, accuracy, precision, and recall were 88.5%, 84.6%, 88.1%, and 88.5%, respectively. The accuracy of the proposed CNN model was significantly higher than that of two orthopedic surgeons (64.0%, p<0.001; 58.0%, p<0.001). Grad-CAM analysis demonstrated that the CNN model most frequently focused on the foramen location for the determination of foraminal stenosis, although disc space was also frequently taken into consideration.

CONCLUSION

A CNN algorithm that can detect neural foraminal stenosis in cervical oblique radiographs was developed. The AUC, F1 score, and accuracy were 0.889, 88.5%, and 84.6%, respectively. With the current CNN model, cervical oblique radiography could be a more effective screening tool for neural foraminal stenosis.

Deep Learning Algorithm Trained on Oblique Cervical Radiographs to Predict Outcomes of Transforaminal Epidural Steroid Injection for Pain from Cervical Foraminal Stenosis

INTRODUCTION

We developed a convolutional neural network (CNN) model to predict treatment outcomes of transforaminal epidural steroid injection (TFESI) for controlling cervical radicular pain due to cervical foraminal stenosis.

METHODS

We retrospectively recruited 293 patients with cervical TFESI due to cervical radicular pain caused by cervical foraminal stenosis. We obtained a single oblique cervical radiograph from each patient. We cut each oblique cervical radiograph image into a square shape, including the foramen that was targeted for TFESI, the intervertebral disc, the facet joint of the corresponding level with the targeted foramen, and the pedicles of the vertebral bodies just above and below the targeted foramen. Therefore, images including the targeted foramen and structures around the targeted foramen were used as input data. A favorable outcome was defined as a ≥ 50% reduction in the numeric rating scale (NRS) score at 2 months post TFESI compared to 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 the treatment outcome of cervical TFESI in patients with cervical foraminal stenosis was 0.823.

CONCLUSION

A CNN model trained using oblique cervical radiographs can be helpful in predicting treatment outcomes after cervical TFESI in patients with cervical foraminal stenosis. If the predictive accuracy is increased, we believe that the deep learning model using cervical radiographs as input data can be easily and widely used in clinics or hospitals.

Anterior cervical tunnectomy and fusion (ACTF): a novel technique for cervical canal decompression

OBJECTIVE

To describe a novel surgical technique note coined as anterior cervical tunnectomy and fusion (ACTF) which applying on removal of posterior vertebral bony protrusions or soft extrusions.

METHODS

Total twenty-three patients from January 2016 to January 2021 who experienced with spinal cord compression and performed by ACTF were retrospectively reviewed. Herein, relevant information including patient's gender, age, BMI, intraoperative time, intraoperative blood loss, postoperative complications and postoperative hospitalized stay were collected. Furthermore, JOA and VAS score were both collected. Moreover, imaging parameters were measured and calculated on radiographs. Correlated data were analyzed by t test. Significance was considered when P < 0.05.

RESULTS

All patients in this study were validated with favorable outcomes and none of postoperative complications. The Nurick grade of patients dramatically deceased postoperation (P < 0.001). And postoperative VAS score of patients (P < 0.001), as well as JOA score (P < 0.001), was given dramatical significance comparing to preoperation. Furthermore, occupying rate (OR) (P < 0.001) was obviously reduced while space available cord (SAC) (P < 0.001) and diameter of spinal cord (P < 0.001) was significantly increased postoperation. Meanwhile, disc height of involved segment, C2-7 SVA, and C2-C7 Cobb angle were measured on sagittal plane of lateral radiograph. Postoperative disc height of involved segment (P < 0.001) significantly elevated comparing to preoperation. However, there were no significance on C2-7 SVA (P = 0.460) and C2-C7 Cobb angle (P = 0.097).

CONCLUSIONS

The novel surgical technique coined by ACTF is a practicable approach during taking charge of bony and soft narrowing behind vertebral space.