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Multimodal Imaging-assisted Diagnosis Model for Cervical Spine Tumors

NCT04959656 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2021-07-13

No results posted yet for this study

Summary

Cervical spine tumor is a small sample of tumor disease with low incidence, great harm, and complex anatomical structure. It is very difficult to identify and classify benign and malignant cervical spine tumors clinically.

The deep learning model we constructed in the early stage has a higher accuracy rate for the image diagnosis of cervical spondylosis with a large number of cases, and a better clinical application effect, but the accuracy rate for cervical spine tumors with a small number of cases is lower. The reason may be the amount of data. With limited tasks, the traditional deep learning model is difficult to play an effective role.

Based on this, we propose to build a small sample-oriented deep learning model to assist clinicians in the diagnosis of cervical spine tumors with multimodal images, and to evaluate the benign and malignant tumors.

Conditions

  • Spine Tumor

Sponsors & Collaborators

  • Peking University Third Hospital

    lead OTHER

Principal Investigators

  • hanqiang ouyang · Peking University Third Hospital

Eligibility

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

Timeline & Regulatory

Start
2020-01-01
Primary Completion
2020-06-01
Completion
2021-06-01

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 NCT04959656 on ClinicalTrials.gov