Deep-learning Based Classification of Spine CT (DETECT)

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 Overview

• Status: Unknown
• Conditions: Surgical Procedure, Unspecified
• Intervention / Treatment: 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 Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

Study Locations

• China
  • Shanghai
    • Shanghai, Shanghai, China, 200072
      • Recruiting
      • Shanghai Tenth People's Hospital
      • Contact: Guoxin Fan; Email: 1610707@tongji.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 65 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

patients with thin layer spinal CT covering targeted level will be included.

Description

Inclusion Criteria:

- spinal thin layer CT

Exclusion Critera:

• medals or other implants induce artifact
• poor image quality

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
classification accuracy	classification accuracy (e.g. area under the curve, etc.)	1 day
segmentation accuracy	segmentation accuracy of multiple structures (e.g. Dice score, etc.)	1 day

Collaborators and Investigators

• Sponsor: Shanghai 10th People's Hospital
• Collaborators: Third Affiliated Hospital, Sun Yat-Sen University
• Investigators: Principal Investigator: Shisheng He, M.D., Shanghai 10th People's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): February 22, 2019
• Primary Completion (Anticipated): May 1, 2020
• Study Completion (Anticipated): May 1, 2020

Study Registration Dates

• First Submitted: November 16, 2018
• First Submitted That Met QC Criteria: December 29, 2018
• First Posted (Actual): January 2, 2019

Study Record Updates

• Last Update Posted (Actual): May 12, 2020
• Last Update Submitted That Met QC Criteria: May 10, 2020
• Last Verified: May 1, 2020

More Information

Terms related to this study

• Other Study ID Numbers: SHSY180624

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No