DETECT: Deep-learning Based Classification of Spine CT

• Sponsor: Shanghai 10th People's Hospital (Other)
• Overall Status: Unknown status
• CT.gov ID: NCT03790930
• Collaborator: Third Affiliated Hospital, Sun Yat-Sen University (Other)
• Enrollment: 500
• Location: 1
• Anticipated Duration (Months): 14.3
• Patients Per Site Per Month: 35.1

Study Details

Study Description

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	Phase
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

• Study Type: Observational
• Anticipated Enrollment: 500 participants
• Observational Model: Case-Only
• Time Perspective: Retrospective
• Official Title: Deep-learning Based Classification of Spine CT
• Actual Study Start Date: Feb 22, 2019
• Anticipated Primary Completion Date: May 1, 2020
• Anticipated Study Completion Date: May 1, 2020

Arms and Interventions

Arm	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

Primary Outcome Measures

1. classification accuracy [1 day]

   classification accuracy (e.g. area under the curve, etc.)

2. segmentation accuracy [1 day]

   segmentation accuracy of multiple structures (e.g. Dice score, etc.)

Eligibility Criteria

Criteria

• Ages Eligible for Study: 18 Years to 65 Years
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Inclusion Criteria:

• spinal thin layer CT

Exclusion Critera:

• medals or other implants induce artifact

• poor image quality

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Shanghai Tenth People's Hospital	Shanghai	Shanghai	China	200072

Sponsors and Collaborators

• Shanghai 10th People's Hospital
• Third Affiliated Hospital, Sun Yat-Sen University

Investigators

• Principal Investigator: Shisheng He, M.D., Shanghai 10th People's Hospital

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• 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: Jan 2, 2019
• Last Update Posted: May 12, 2020
• Last Verified: May 1, 2020

• Individual Participant Data (IPD) Sharing Statement: Undecided
• Plan to Share IPD: Undecided
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Study Results

No Results Posted as of May 12, 2020