Background: ACDF is the standard procedure for treatment of cervical spondylotic myelopathy (CSM), but a long-term follow-up has been revealed some associated complications of swallowing discomfort, displacement of the fusion device, and accelerated degeneration of the adjacent segment. Objective: To evaluate the clinical outcomes of anterior percutaneous full-endoscopic transcorporeal decompression of the spinal cord (APFETDSC) for single-segment CSM and to analyze the clinical efficacy, surgical characteristics, and complication prevention. Methods: A total of 32 patients who underwent APFETDSC for single-segment CSM from Aug. 2015 to Apr. 2017 were reviewed. Operating time, time of walking out of bed postoperation, length of hospitalization, complications, neck pain visual analog scale (VAS), and Japanese Orthopaedic Association Score (JOA) were evaluated. Measurement of intervertebral height (HI) of surgical segments on cervical neutral X-ray, Harrison’s method was used to measure cervical spine angle (CSA). Results: The operation time was 103.3 ± 12.95 min, time of walking out of bed after surgery was 19.81 ± 4.603 h, the length of postoperative hospital stay was 57.48 ± 19.48 h. The postoperative neck pain VAS and JOA were significantly improved compared with preoperation(p < 0.001). The postoperative HI was statistical significance decreased compared with preoperation(p < 0.001), but the HI reduction was less than 0.5 mm, without adverse clinical symptoms. The postoperative CSA was significantly improved compared with preoperative(p < 0.001). The excellent and good rate was 87.5%, and the JOA improvement rate was 75.52 ± 11.11%. There was no cervical instability, vertebral fracture, wound infection, and other complications. Conclusions: APFETDSC is a safe and effective minimally invasive technique with small auxiliary injuries for single-segment CSM while avoiding the sequelae of ACDF. Its short-term clinical efficacy was good and no significant effect on cervical stability.

Objective: This systematic review evaluates the performance and limitations of AI-based models for Degenerative cervical diseases (DCD) diagnosis using MRI. Methods: A comprehensive literature search was conducted in three databases—PubMed, Embase, and Web of Science—covering studies published between January 2010 and March 2024. Studies were included if they employed AI techniques for the diagnosis or prognosis of DCD using MRI. Key performance metrics, methodological details, and limitations were extracted and analyzed. Results: Eleven studies met the inclusion criteria, with AI models showing high diagnostic performance. Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). Comparative analysis revealed that CNN-based approaches showed consistently high performance in ossification of the posterior longitudinal ligament detection, while traditional machine learning methods demonstrated varying effectiveness in cervical spondylotic myelopathy classification. Sample sizes varied significantly, ranging from 28 to 900 patients. MRI protocols also differed across studies, with variations in field strengths, slice thicknesses, and sequences used. Seven studies assessed inter-rater reliability. Most studies lacked external validation, which raises concerns about the generalizability of the models. Additionally, hardware configurations were inconsistently reported, and data augmentation techniques were underutilized, limiting the robustness of the models in smaller datasets. Conclusion: While AI models for DCD diagnosis using MRI show high diagnostic potential, methodological weaknesses such as insufficient external validation and small sample sizes hinder broader clinical adoption. Future research should focus on larger, standardized, multi-center studies to improve the robustness and clinical relevance of AI-driven tools for DCD diagnosis.

Study Design.: A retrospective two-center external validation study was conducted at two medical centers, collecting cervical spine MRI data from patients suspected of degenerative cervical myelopathy (DCM) between March 2022 and August 2024, forming a consecutive series with external validation. Objective.: To develop and validate a deep learning model utilizing YOLO11 architecture for automated detection of cervical spinal cord compression on MRI and evaluate its performance against expert annotations. Summary of Background Data.: DCM represents the leading cause of nontraumatic spinal cord injury in adults. While MRI facilitates early detection and provides the foundation for timely intervention, image interpretation remains subjective and dependent on physician experience, resulting in diagnostic variability and challenges in clinical consistency. Materials and Methods.: A YOLO11-based deep learning model was implemented with a binary classification scheme (Normal vs . Compression). Five physicians annotated 1431 sagittal T2-weighted cervical MRI images from 735 patients using standardized protocols, achieving excellent interobserver agreement. The data set comprised training/validation sets (577 patients, 1141 images), an internal test set (64 patients, 115 images), and an external test set (94 patients, 175 images). Five-fold cross-validation assessed model robustness. Standardized preprocessing incorporating contrast enhancement, noise reduction, and normalization was applied. Gradient-weighted Class Activation Mapping enhanced model interpretability. Results.: Five-fold cross-validation yielded consistent performance with mAP50 ranging from 0.917 to 0.970, precision from 0.897 to 0.923, and recall from 0.922 to 0.946. External testing demonstrated statistically superior agreement with expert annotations (mAP50=0.944, 95% CI: 0.934–0.953) compared with mid-level physician annotations (mAP50=0.912, 95% CI: 0.908–0.919), with the difference being statistically significant (95% CI of difference: 0.015–0.043, P <0.05). Conclusion: The YOLO11-based model demonstrated stable two-center performance with close alignment to expert-level clinical standards. The rapid inference, high sensitivity, and integrated visualization system address key challenges related to efficiency and interpretability in clinical AI applications for cervical spinal cord compression assessment.

BackgroundWith the development of spinal endoscopic techniques, on the basis of our previous experience in treating various types of cervical disc herniation with this endoscopic technique, we took the lead in applying the percutaneous fully endoscopic anterior transcorporeal procedure to be utilized in the treatment of the isolated cervical ossification of the posterior longitudinal ligament (OPLL).Case PresentationA 66-year-old male patient who weighed 57 kg, with a height of 169 cm was admitted to the hospital on September 16, 2021 because of recurrent pain and numbness in the neck, shoulder, and right arm for 2 years, which as aggravated for the last 2 weeks. Two years ago, the patient developed neck and shoulder pain accompanied by right arm pain without obvious predisposing factors, and numbness in the first web space of the right hand. In the last 2 weeks, he had difficulty moving the right arm, but no pain or numbness in the contralateral arm. MRI and CT scans demonstrated that the ossified posterior longitudinal ligament of the cervical 5/6 vertebrae with spinal canal stenosis and seriously compressed the spinal cord patient was treated with a percutaneous fully endoscopic anterior transcorporeal procedure.ConclusionOur percutaneous fully endoscopic anterior transcorporeal procedure is a feasible, minimally invasive surgery for treating isolated ossification of the posterior longitudinal ligament in the cervical spine. With the development of spinal endoscopic techniques, we took the lead in applying the percutaneous fully endoscopic anterior transcorporeal procedure to be utilized in the treatment of the isolated cervical ossification of the posterior longitudinal ligament (OPLL).image

Cervical ossification of the posterior longitudinal ligament (OPLL) causes spinal cord compression due to spinal canal stenosis. Traditional anterior appro

Background Magnetic resonance imaging (MRI) is the reference standard for evaluating degenerative lumbar spine disorders, but interpretation is time-consuming and subject to inter-observer variability. SpineNetv2, a publicly available deep learning system, enables automated analysis of multiple spinal pathologies. This study conducted an independent external validation of SpineNetv2 against expert reference assessments. Methods A total of 491 patients (2,455 lumbar discs, L1/2–L5/S1) were retrospectively included. Disc-level reference assessments were provided by an expert orthopedic surgeon, with a junior orthopedic surgeon serving as comparator. Six pathologies were assessed: disc degeneration (Pfirrmann grading), central canal stenosis (CCS), spondylolisthesis, herniation, and bilateral foraminal stenosis (FS). Performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1-score, Matthews correlation coefficient, exact agreement, weighted kappa, and mean absolute error. McNemar’s test and bootstrap resampling (1,000 iterations) were used for statistical analysis. Results Overall agreement ranged from 83.5% to 97.5% (mean 92.8%). SpineNetv2 significantly outperformed the junior orthopedic surgeon in CCS, spondylolisthesis, and bilateral FS (all p ≤ 0.001), with comparable performance in herniation (p = 0.293). Pfirrmann grading showed lower MAE for SpineNetv2 compared with the junior surgeon (0.213 vs. 0.254, p = 0.001), though agreement declined in older patients and upper lumbar discs. Error analysis revealed a specificity-oriented profile, with false negatives exceeding false positives. Conclusions SpineNetv2 demonstrated high agreement across five binary lumbar pathologies, while Pfirrmann grading remained the main limitation, particularly in elderly upper lumbar discs. Its specificity-oriented profile supports use as a confirmatory second reader, but reliance on negative findings is not recommended. Broader reliability will require multicenter, multi-reader validation and sensitivity-oriented calibration.