Deep-learning Based Classification of Spine CT

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT03790930

Acronym

DETECT

Enrollment

500

Registered

2019-01-02

Start date

2019-02-22

Completion date

2020-05-31

Last updated

2020-05-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Surgical Procedure, Unspecified

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.

Interventions

DIAGNOSTIC_TESTdeep learning

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

Study design

Observational model

CASE_ONLY

Time perspective

RETROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 65 Years

Healthy volunteers

Inclusion criteria

\- spinal thin layer CT Exclusion Critera: * medals or other implants induce artifact * poor image quality

Design outcomes

Primary

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

Countries

China

Contacts

Primary ContactGuoxin Fan

gfan@tongji.edu.cn008602166307580

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026