Occasionally, ossification of the posterior longitudinal ligament (OPLL) causes cord compression, resulting in cervical myelopathy. OPLL differs from other causes of cervical spondylotic myelopathy in several ways, and the surgical strategy should be chosen with OPLL’s characteristics in mind. Although both the anterior and posterior approaches are effective surgical methods for the treatment of OPLL cervical myelopathy, they each have their own set of benefits and drawbacks. Anterior decompression and fusion (ADF) may improve neurological recovery, restore lordosis, and prevent OPLL mass progression. The benefits can be seen in patients with a high canal occupying ratio or kyphotic alignment. We discussed the benefits, limitations, indications, and surgical techniques of ADF for the treatment of OPLL-induced cervical myelopathy in this narrative.

Background: Despite the suitable characteristics of ropivacaine as an epidural analgesic agent, such as better preservation of motor function and less neurotoxicity, we are aware of no data on its clinical application in pain management following lumbar spine surgery. The purpose of the present study was to evaluate the preemptive analgesic effects and safety of a single epidural injection of ropivacaine during lumbar arthrodesis. Methods: We performed a randomized, double-blinded, intention-to-treat study. Patients with planned one-level posterior lumbar interbody arthrodesis were randomly assigned to either the injection group (n = 32) or the control group (n = 34). The injection group received a 10-mL epidural injection of 0.1% ropivacaine twenty minutes before the skin incision at the planned vertebral level, and the control group received an epidural injection of 10 mL of 0.9% saline solution. A numeric rating scale (from 0 to 10) was measured at seven time points after surgery (at two, four, eight, twelve, twenty-four, and forty-eight hours and at the time of discharge), and the frequency of pushed-button patient-controlled analgesia and total fentanyl consumption were assessed at similar time points (up to two, up to four, up to eight, up to twelve, up to twenty-four, and up to forty-eight hours after surgery). Postoperative nausea and vomiting, the duration of the hospital stay, and the Likert satisfaction score at the time of discharge were evaluated. Results: There were no significant differences between the two groups preoperatively. The numeric rating scale score was higher until twelve hours (p < 0.05) and the frequency of button pushes was higher at every time point except eight to twelve hours (p < 0.05) in the control group as compared with the injection group. Fentanyl consumption until eight to twelve hours (p < 0.05) and total consumption (p < 0.001) at discharge were higher in the control group. There were no differences between the two groups in terms of postoperative nausea and vomiting, the duration of hospital stay, or the mean satisfaction score, and no transient motor weakness was seen in relation to epidural injection of ropivacaine. Conclusions: A single-dose epidural injection of 0.1% ropivacaine before lumbar spine surgery is effective for reducing early postoperative pain without related complications such as transient motor weakness. Level of Evidence: Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

Various disease detection models, based on deep learning algorithms using medical radiograph images (MRI, CT, and X-ray), have been actively explored in relation to medicine and computer vision. For diseases related to the spine, primarily MRI-based or CT-based studies have been conducted, but most studies were associated with the lumbar spine, not the cervical spine. Foraminal stenosis offers important clues in diagnosing cervical radiculopathy, which is usually detected based on MRI data because it is difficult even for experts to diagnose using only an X-ray examination. However, MRI examinations are expensive, placing a potential burden on patients. Therefore, this paper proposes a novel model for diagnosing foraminal stenosis using only X-ray images. In addition, we propose methods suitable for cervical spine X-ray images to improve the performance of the proposed classification model. First, the proposed model adopts data preprocessing and augmentation methods, including Histogram Equalization, Flip, and Spatial Transformer Networks. Second, we apply fine-tuned transfer learning using a pre-trained ResNet50 with cervical spine X-ray images. Compared to the basic ResNet50 model, the proposed method improves the performance of foraminal stenosis diagnosis by approximately 5.3–6.9%, 5.2–6.5%, 5.4–9.2%, and 0.8–4.3% in Accuracy, F1 score, specificity, and sensitivity, respectively. We expect that the proposed model can contribute towards reducing the cost of expensive examinations by detecting foraminal stenosis using X-ray images only.