Deep-learning Based Classification of Spine CT

Surgical Procedure, Unspecified Clinical Trial

— DETECT  

Official title:

Deep-learning Based Classification of Spine CT

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT03790930
• Other study ID #: SHSY180624
• Status: Recruiting
• Start date: February 22, 2019
• Est. completion date: May 2020

Study information

• Verified date: May 2020
• Source: Shanghai 10th People's Hospital
• Contact: Guoxin Fan
• Phone: 008602166307580
• Email: gfan[@]tongji.edu.cn
• Is FDA regulated: No
• Study type: Observational

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

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 500
• Est. completion date: May 2020
• Est. primary completion date: May 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 65 Years

Eligibility

Inclusion Criteria:

• spinal thin layer CT

Exclusion Critera:

• medals or other implants induce artifact

• poor image quality

Related Conditions & MeSH terms

• Surgical Procedure, Unspecified

Intervention

Diagnostic Test:
• deep learning
manually labeled samples will be used to train, validate and test deep learning algorithm, and then realize automatic classification.

Locations

Country	Name	City	State
China	Shanghai Tenth People's Hospital	Shanghai	Shanghai

Sponsors (2)

Lead Sponsor	Collaborator
Shanghai 10th People's Hospital	Third Affiliated Hospital, Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	classification accuracy	classification accuracy (e.g. area under the curve, etc.)	1 day
Primary	segmentation accuracy	segmentation accuracy of multiple structures (e.g. Dice score, etc.)	1 day