The purpose of this study:

1. Build an artificial intelligence screening tool for cervical spondylosis based on hand function analysis;
2. Verify the accuracy of cervical spondylosis screening tools.

Cervical spondylotic myelopathy (CSM) has a high incidence in the middle-aged and elderly people. According to clinical research, the surgeon can makes a preliminary assessment of the severity of CSM based on a 10-second grip and release (G&R) test. The statistics of G&R test mainly 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. We construct and verify the intelligent video analysis system for Grip-&-release. The system has a high accuracy and inference speed that ensure 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.

Inclusion Criteria:

1. Age between 18-80 years old;
2. Patients who have received cervical CT or MR examination within the last month and have been clearly diagnosed or excluded from cervical spondylosis (i.e., those who have been clearly diagnosed before participating in the study);
3. People who are willing to participate in the study with CT/MR examination (i.e., those who have not been diagnosed before joining the study).

Exclusion Criteria:

1. Subjects with cervical spondylosis who have been examined but still cannot be definitively diagnosed or ruled out;
2. Subjects with definite presence of hand trauma;
3. People who do not have access to CT or MR;
4. Subjects who did not comply with the examination, were unable to perform neurological assessment, or were unable to fill out questionnaires.

1000 asymptomatic population in community; 200 cases of cervical spondylosis; Arthritis, Parkinson's disease, cerebral apoplexy 60 patients each.