Automatic Diagnosis of Spinal Stenosis on CT

NCT ID: NCT03746561

Last Updated: 2018-11-19

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-11-30
• Study Completion Date: 2019-05-31

Brief Summary

MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis.

Detailed Description

MRI is a common tool for radiographic diagnosis of spinal stenosis, but it is expensive and requires long scanning time. CT is also a useful tool to diagnose spinal stenosis, yet interpretation can be time-consuming with high inter-reader variability even among the most specialized radiologists. In this study, the investigators aim to develop a deep-learning algorithm to automatically detect and classify lumbar spinal stenosis. It would be a time-saving workflow if the software can assist the radiologists to detect and locate the suspected lesion.

Conditions

Spinal Stenosis

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Study Groups

spinal stenosis

Spinal stenosis is a narrowing of the spaces within your spine, which can put pressure on the nerves that travel through the spine. Spinal stenosis occurs most often in the lower back and the neck.

deep learning

Intervention Type: DIAGNOSTIC_TEST

detect and classify spinal stenosis by deep learning

Interventions

deep learning

detect and classify spinal stenosis by deep learning

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• Age >18 years
• with radiologists' CT reports on cervical, thoracic and lumbar stenosis

Exclusion Criteria

• not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Brigham and Women's Hospital

OTHER

Sponsor Role: collaborator

Shanghai East Hospital

OTHER

Sponsor Role: collaborator

Shanghai Tongji Hospital, Tongji University School of Medicine

OTHER

Sponsor Role: collaborator

Shanghai 10th People's Hospital

OTHER

Sponsor Role: lead

Responsible Party

Shisheng He, MD

Deputy Director of Orthopedic Department

Responsibility Role: PRINCIPAL_INVESTIGATOR

Central Contacts

Shisheng He, MD

Role: CONTACT

TJHSS7418@TONGJI.EDU.CN

021-66307580

GUOXIN FAN, MD

Role: CONTACT

GFAN@TONGJI.EDU.CN

021-66307580

Other Identifiers

SHSY181022

Identifier Type: -

Identifier Source: org_study_id