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Automatic Diagnosis of Spinal Stenosis on CT

NCT03746561 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2018-11-19

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

deep learning

detect and classify spinal stenosis by deep learning

Sponsors & Collaborators

  • Brigham and Women's Hospital

    collaborator OTHER

  • Shanghai East Hospital

    collaborator OTHER

  • Shanghai Tongji Hospital, Tongji University School of Medicine

    collaborator OTHER

  • Shanghai 10th People's Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-11-30
Primary Completion
2019-04-30
Completion
2019-05-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03746561 on ClinicalTrials.gov