Automatic Diagnosis of Spinal Stenosis on CT

NCT03746561 | Unknown DMC

• 1 other identifier: SHSY181022
• Study Type: observational
• Target: 500
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Spinal Stenosis

Outcome Measures

Primary Outcomes (1)

• diagnostic accuracy of deep learning

  Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT

  1 day

Secondary Outcomes (1)

• Diagnostic Performance of deep learning

  1 day

Study Arms (1)

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.

Diagnostic Test: deep learning

Interventions

deep learning DIAGNOSTIC_TEST

detect and classify spinal stenosis by deep learning

spinal stenosis

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Spinal stenosis is a narrowing of the spaces within the spine, which can put pressure on the nerves that travel through the spine. Spinal stenosis occurs most often in the back, the neck, and sometimes the thoracic spine. Some people with spinal stenosis may not have symptoms. Others may experience pain, tingling, numbness and muscle weakness. Symptoms can worsen over time.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Shanghai 10th People's Hospital: lead
• Brigham and Women's Hospital: collaborator
• Shanghai East Hospital: collaborator
• Shanghai Tongji Hospital, Tongji University School of Medicine: collaborator

MeSH Terms

Conditions

Spinal Stenosis

Interventions

Deep Learning

Condition Hierarchy (Ancestors)

Spinal Diseases; Bone Diseases; Musculoskeletal Diseases

Intervention Hierarchy (Ancestors)

Machine Learning; Artificial Intelligence; Algorithms; Mathematical Concepts; Neural Networks, Computer

Central Study Contacts

Shisheng He, MD

CONTACT

021-66307580; TJHSS7418@TONGJI.EDU.CN

GUOXIN FAN, MD

CONTACT

021-66307580; GFAN@TONGJI.EDU.CN

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy Director of Orthopedic Department

Study Record Dates

• First Submitted: November 7, 2018
• First Posted: November 19, 2018
• Study Start: November 1, 2018
• Primary Completion: April 1, 2019
• Study Completion: May 1, 2019
• Last Updated: November 19, 2018

Record last verified: 2018-11