Deep-learning Based Classification of Spine CT

NCT ID: NCT03790930

Last Updated: 2020-05-12

Basic Information

• Recruitment Status: UNKNOWN
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2019-02-22
• Study Completion Date: 2020-05-31

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.

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

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: RETROSPECTIVE

Study Groups

thin layer CT

Thin-layer CT will be manually labeled and used to train, validate and test deep learning algorithm.

deep learning

Intervention Type: DIAGNOSTIC_TEST

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

Interventions

deep learning

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

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

• spinal thin layer CT

Exclusion Critera:

• medals or other implants induce artifact
• poor image quality

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Third Affiliated Hospital, Sun Yat-Sen University

OTHER

Sponsor Role: collaborator

Shanghai 10th People's Hospital

OTHER

Sponsor Role: lead

Responsible Party

Shisheng He, MD

Executive Director of Orthopedic Department

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Shisheng He, M.D.

Role: PRINCIPAL_INVESTIGATOR

Shanghai 10th People's Hospital

Locations

Shanghai Tenth People's Hospital

Shanghai, Shanghai Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Guoxin Fan

Role: CONTACT

gfan@tongji.edu.cn

008602166307580

Facility Contacts

Guoxin Fan

Role: primary

1610707@tongji.edu.cn

Other Identifiers

SHSY180624

Identifier Type: -

Identifier Source: org_study_id