Surgical Procedure, UnspecifiedDETECTNCT03790930
Deep-learning Based Classification of Spine CT
An observational study of Surgical Procedure, Unspecified, run by Shanghai 10th People's Hospital at 1 site in China.
Trial summary
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Surgical Procedure, Unspecified
- What is being tested?
- deep learning
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 22 Feb 2019to May 2020
- 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 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: patients with thin layer spinal CT covering targeted level will be included.
Who may be able to join
- spinal thin layer CT
Who cannot join
- medals or other implants induce artifact
- poor image quality
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Shanghai Tenth People's Hospital
Shanghai, Shanghai Municipality, 200072
Guoxin Fan, 1610707@tongji.edu.cn
About this study
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
Read the detailed description
Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic and treatment of spinal disease. Classification of specific targets (e.g. individuals, lesions, etc.) is one of the most common mission of medical image analysis. However, it is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
- Conditions
- Surgical Procedure, Unspecified
- Being tested
- deep learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
deep learningDiagnostic test
manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.
| Study group | Role | What they receive |
|---|---|---|
| thin layer CT | Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm. Receives: Diagnostic Test: deep learning |
What is being measured
The primary outcome is the main question the study is designed to answer.
classification accuracy
classification accuracy (e.g. area under the curve, etc.)
Measured: 1 day
segmentation accuracy
segmentation accuracy of multiple structures (e.g. Dice score, etc.)
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 posted2 Jan 2019
- Study start22 Feb 2019
- Primary completionMay 2020 (estimated)
- Study completionMay 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Surgical Procedure, UnspecifiedStudies 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
- Third Affiliated Hospital, Sun Yat-Sen University
- Responsible party
- Principal investigator, Shisheng He, MD, Shanghai 10th People's Hospital
- Investigators
- Shisheng He, M.D., Principal investigator, Shanghai 10th People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03790930
- Sponsor's protocol ID
- SHSY180624
- Acronym
- DETECT
- Status last verified
- May 2020
- First submitted
- 16 Nov 2018
- Last updated
- 12 May 2020
- Results posted
- No
- Data monitoring committee
- No
- 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 12 May 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Deep-learning Based Classification of Spine CT
- ClinicalTrials.gov ID
- NCT03790930
- Acronym / sponsor ID
- DETECT / SHSY180624
- Conditions
- Surgical Procedure, Unspecified
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Shanghai 10th People's HospitalUniversity, hospital or other
- Collaborators
- Third Affiliated Hospital, Sun Yat-Sen University
- Study start
- 22 Feb 2019 (actual)
- Primary completion
- May 2020 (estimated)
- Study completion
- May 2020 (estimated)
- First posted
- 2 Jan 2019 (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.
It is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
Detailed description
Computer tomography (CT) is one of the most important imaging tool to assist the diagnostic and treatment of spinal disease. Classification of specific targets (e.g. individuals, lesions, etc.) is one of the most common mission of medical image analysis. However, it is time-consuming for spine surgeons or radiologists to conduct manual classifications of spinal CT, which may also be correlated with high inter-observer variance. With the development of computer science, deep learning has emerged as a promising technique to classify images from individual level to pixel level. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| thin layer CT | – | Diagnostic Test: deep learningThin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- patients with thin layer spinal CT covering targeted level will be included.
Key inclusion criteria (1 listed)
- spinal thin layer CT
Key exclusion criteria (2 listed)
- medals or other implants induce artifact
- poor image quality
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 learningmanually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification. | Diagnostic test | thin layer CT |
Primary outcome
classification accuracy
Time frame: 1 day
classification accuracy (e.g. area under the curve, etc.)
segmentation accuracy
Time frame: 1 day
segmentation accuracy of multiple structures (e.g. Dice score, etc.)
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 May 2020.
- 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.
Shisheng He, M.D. principal investigator
Shanghai 10th People's Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Surgical Procedure, Unspecified, run by Shanghai 10th People's Hospital at 1 site in China.
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 1 inclusion and 2 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 12 May 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
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 Guoxin Fan (008602166307580, gfan@tongji.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
classification accuracy (time frame: 1 day). The study lists 2 primary outcomes in all. 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 12 May 2020.