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Deep-learning Based Classification of Spine CT (DETECT)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03790930
Recruitment Status : Recruiting
First Posted : January 2, 2019
Last Update Posted : May 12, 2020
Sponsor:
Collaborator:
Third Affiliated Hospital, Sun Yat-Sen University
Information provided by (Responsible Party):
Shisheng He, MD, Shanghai 10th People's Hospital

Study Description
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Brief 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. The main of the study is to automatically identify and classify the lesions, or segment targeted structures on spinal CT with deep learning.

Condition or disease Intervention/treatment
Surgical Procedure, Unspecified Diagnostic Test: 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
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Study Type : Observational
Estimated Enrollment : 500 participants
Observational Model: Case-Only
Time Perspective: Retrospective
Official Title: Deep-learning Based Classification of Spine CT
Actual Study Start Date : February 22, 2019
Estimated Primary Completion Date : May 2020
Estimated Study Completion Date : May 2020
Groups and Cohorts
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Group/Cohort Intervention/treatment
thin layer CT
Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm.
 Diagnostic Test: deep learning
manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.


Outcome Measures
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Primary Outcome Measures :
  1. classification accuracy [ Time Frame: 1 day ]
    classification accuracy (e.g. area under the curve, etc.)

  2. segmentation accuracy [ Time Frame: 1 day ]
    segmentation accuracy of multiple structures (e.g. Dice score, etc.)


Eligibility Criteria
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Ages Eligible for Study:   18 Years to 65 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
patients with thin layer spinal CT covering targeted level will be included.
Criteria

Inclusion Criteria:

- spinal thin layer CT

Exclusion Critera:

  • medals or other implants induce artifact
  • poor image quality
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03790930


Contacts
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Contact: Guoxin Fan 008602166307580 gfan@tongji.edu.cn

Locations
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China, Shanghai
Shanghai Tenth People's Hospital Recruiting
Shanghai, Shanghai, China, 200072
Contact: Guoxin Fan       1610707@tongji.edu.cn   
Sponsors and Collaborators
Shanghai 10th People's Hospital
Third Affiliated Hospital, Sun Yat-Sen University
Investigators
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Principal Investigator: Shisheng He, M.D. Shanghai 10th People's Hospital
More Information
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Responsible Party: Shisheng He, MD, Executive Director of Orthopedic Department, Shanghai 10th People's Hospital
ClinicalTrials.gov Identifier: NCT03790930    
Other Study ID Numbers: SHSY180624
First Posted: January 2, 2019    Key Record Dates
Last Update Posted: May 12, 2020
Last Verified: May 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No