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Deep-learning Based Classification of Spine CT

NCT03790930 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 500

Last updated 2020-05-12

No results posted yet for this study

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.

Conditions

  • Surgical Procedure, Unspecified

Interventions

DIAGNOSTIC_TEST

deep learning

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

Sponsors & Collaborators

  • Third Affiliated Hospital, Sun Yat-Sen University

    collaborator OTHER

  • Shanghai 10th People's Hospital

    lead OTHER

Principal Investigators

  • Shisheng He, M.D. · Shanghai 10th People's Hospital

Eligibility

Min Age
18 Years
Max Age
65 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-02-22
Primary Completion
2020-05-31
Completion
2020-05-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03790930 on ClinicalTrials.gov