Accuracy of Realigned K-line for Predicting Surgical Outcomes After Laminoplasty in Patients With Degenerative Cervical Myelopathy.

STUDY DESIGN: A prospective cohort study. OBJECTIVES: To report a new index, the realigned K-line, for predicting surgical outcomes after laminoplasty in patients with degenerative cervical myelopathy (DCM). METHODS: One hundred twenty-eight patients with DCM undergoing laminoplasty were enrolled from January 2018 to April 2021 in our department. A realigned K-line was defined as the line connecting the midpoints of the spinal cord between C2 and C7 on realigned T1-weighted magnetic resonance imaging. The minimum interval between the anterior compression factors of the spinal cord and the realigned K-line (INTrea), and the modified K-line (INTmod) were measured. A logistic regression analysis was performed to identify factors associated with unsatisfactory surgical outcomes. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) was applied to evaluate the reliability of the multivariate logistic regression model. RESULTS: Univariate analysis showed that the score for the bladder function section of the Japanese Orthopedic Association Cervical Myelopathy Evaluation Questionnaire, numeric rating scale scores for arm pain, and INTrea might be related to the Japanese Orthopaedic Association (JOA) recovery rate (RR) not achieving the minimal clinically important difference (MCID) (P < .05). Only INTrea (odds ratio = .744, P < .05) was an independent preoperative factor related to the JOA RR not achieving the MCID (area under the curve, .743). A cutoff of 5.0 mm for INTrea had an accuracy of 71.9% and specificity of 80.3% for predicting the JOA RR not achieving the MCID. CONCLUSIONS: INTrea is an independent preoperative risk factor related to the JOA RR not achieving the MCID in patients with DCM. A cutoff point of 5.0 mm is most appropriate for alerting spine surgeons to a high likelihood of the JOA RR not achieving the MCID.

Severity Assessment of Cervical Spondylotic Myelopathy Based on Intelligent Video Analysis.

Cervical spondylotic myelopathy (CSM) has a high incidence in the middle-aged and elderly people. According to clinical research, there is a connection between hand dexterity and cervical nerves. So the surgeon makes a preliminary assessment of the severity of CSM based on a 10-second grip and release (G&R) test. At present, the statistics of G&R test rely on the surgeon's manual counting. When a patient's hand motion speed is too fast, the surgeon's manual counting is prone to error, leading to potential misdiagnosis. On the other hand, in recent years, artificial intelligence has been developed rapidly, where three-dimensional convolutional neural networks (3D-CNNs) have been widely used in video analysis. This work proposes a hand motion analysis model using a 3D-CNN combined with a de-jittering mechanism to assess the severity of CSM on 10-second G&R videos. We collect 1500 10-second G&R videos recorded by 750 subjects to establish a dataset. The proposed model using 3D-MobileNetV2 as the classifier obtains a Levenshtein accuracy of 97.40% and an average GPU inference time of 3.31 seconds for each 10-second G&R video. Such accuracy and inference speed ensure that the proposed model can be used as a screening examination tool for CSM and a medical assistance tool to help decision making during CSM treatment planning.