Spinal StenosisASSISTNCT03746561
Automatic Diagnosis of Spinal Stenosis on CT
An observational study of Spinal Stenosis, run by Shanghai 10th People's Hospital.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Spinal Stenosis
- What is being tested?
- deep learning
- How many people
- 500 participants
- Where
- No sites listed yet
- Study dates
- Nov 2018to May 2019
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- Age >18 years
- with radiologists' CT reports on cervical, thoracic and lumbar stenosis
Who cannot join
- not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).
Trial locations and contacts
No sites are listed yet.
About this study
Official title: 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.
Read the 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
- Being tested
- deep learning
- Medical subject headings
- Spinal Stenosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
deep learningDiagnostic test
detect and classify spinal stenosis by deep learning
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: deep learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
diagnostic accuracy of deep learning
Diagnostic accuracy of deep learning to determine spinal stenosis compared with radiologists' labels based on CT
Measured: 1 day
Secondary outcomes (1)
Diagnostic Performance of deep learning
Sensitivity, specificity, positive predictive value and negative predictive value of deep learning compared with radiologists' labels based on CT
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Nov 2018
- Study startNov 2018 (estimated)
- Primary completionApr 2019 (estimated)
- Study completionMay 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Spinal StenosisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shanghai 10th People's Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Brigham and Women's Hospital, Shanghai East Hospital, Shanghai Tongji Hospital, Tongji University School of Medicine
- Responsible party
- Principal investigator, Shisheng He, MD, Shanghai 10th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03746561
- Sponsor's protocol ID
- SHSY181022
- Acronym
- ASSIST
- Status last verified
- Nov 2018
- First submitted
- 7 Nov 2018
- Last updated
- 19 Nov 2018
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Diagnosis of Spinal Stenosis on CT With Deep Learning
- ClinicalTrials.gov ID
- NCT03746561
- Acronym / sponsor ID
- ASSIST / SHSY181022
- Conditions
- Spinal Stenosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Shanghai 10th People's HospitalUniversity, hospital or other
- Collaborators
- Brigham and Women's Hospital, Shanghai East Hospital, Shanghai Tongji Hospital, Tongji University School of Medicine
- Study start
- Nov 2018 (estimated)
- Primary completion
- Apr 2019 (estimated)
- Study completion
- May 2019 (estimated)
- First posted
- 19 Nov 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| spinal stenosis | – | Diagnostic Test: deep learningSpinal 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- Age >18 years
- with radiologists' CT reports on cervical, thoracic and lumbar stenosis
Key exclusion criteria (1 listed)
- not applicable (only specific levels with extensive infections, fractures, tumor, high-grade spondylolisthesis would be excluded for analysis).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| deep learningdetect and classify spinal stenosis by deep learning | Diagnostic test | spinal stenosis |
Primary outcome
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 secondary outcome
Diagnostic Performance of deep learning
Time frame: 1 day
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 Nov 2018.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Questions about this trial
What is this clinical trial studying?
An observational study of Spinal Stenosis, run by Shanghai 10th People's Hospital.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 19 Nov 2018. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Shisheng He, MD (021-66307580, TJHSS7418@TONGJI.EDU.CN), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
diagnostic accuracy of deep learning (time frame: 1 day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 19 Nov 2018; AzTrials last refreshed its copy on 6 Nov 2026.