Automatic Diagnosis of Spinal Stenosis on CT (ASSIST)

November 16, 2018 updated by: Shisheng He, MD, Shanghai 10th People's Hospital

Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning

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.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

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.

Study Type

Observational

Enrollment (Anticipated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

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.

Description

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).

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Only
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
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
detect and classify spinal stenosis by deep learning

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
diagnostic accuracy of deep learning
Time Frame: 1 day
Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT
1 day

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Diagnostic Performance of deep learning
Time Frame: 1 day
Sensitivity, specificity, positive predictive value and negative predictive value of deep learning compared with radiologists' labels based on CT
1 day

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai 10th People's Hospital

Collaborators

Brigham and Women's Hospital
Shanghai East Hospital
Shanghai Tongji Hospital, Tongji University School of Medicine

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

November 1, 2018

Primary Completion (Anticipated)

April 1, 2019

Study Completion (Anticipated)

May 1, 2019

Study Registration Dates

First Submitted

November 7, 2018

First Submitted That Met QC Criteria

November 16, 2018

First Posted (Actual)

November 19, 2018

Study Record Updates

Last Update Posted (Actual)

November 19, 2018

Last Update Submitted That Met QC Criteria

November 16, 2018

Last Verified

November 1, 2018

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SHSY181022

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

Undecided

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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