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Research on the application of deep learning model in the diagnosis of degenerative cervical myelopathy

Research on the application of deep learning model in the diagnosis of degenerative cervical myelopathy

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400087932
Enrollment
Unknown
Registered
2024-08-07
Start date
2024-01-01
Completion date
Unknown
Last updated
2024-08-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

degenerative cervical myelopathy

Interventions

degenerative cervical myelopathy:None

Sponsors

Spine Center, Department of Orthopedics, Changzheng Hospital, Naval Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: This study collected the cervical spine magnetic resonance images (Philips Ingenia 3.0T fully digital magnetic resonance system, DICOM) and corresponding imaging diagnosis of Shanghai Changzheng Hospital from 2018 to 2023 in physical examination, outpatient, emergency, and inpatient patients to establish cervical spine imaging database.

Exclusion criteria

Exclusion criteria: The exclusion criteria for this study are: a. Patients who underwent cervical spine surgery; b. Trauma, deformity or tumor suspected in imaging diagnosis; c. Age less than18 years old or more than 80 years old; d. Poor image quality; e. T2WI sagittal plane without complete C2-C7 Segmental cervical spinal cord imaging

Design outcomes

Primary

MeasureTime frame
Classification of cervical spinal stenosis;—

Secondary

MeasureTime frame
area of cerebrospinal fluid;Spinal cord deformation rate;—

Countries

China

Contacts

Public ContactHuajiang Chen

Spine Center, Department of Orthopedics, Changzheng Hospital, Naval Medical University

spine_czchenhj@163.com+86 138 1855 9892

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026