Clinical Trials Logo

Clinical Trial 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.


Clinical Trial Description

Cervical spine tumor is a small-sample 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 is suitable for the large number of cases. The imaging diagnosis of cervical spondylosis has a high accuracy rate and a good clinical application effect, but the accuracy rate is low for cervical spine tumors with a small number of cases. The reason may be that for tasks with limited amount of data, the traditional deep learning model is difficult to play an effective role. Based on this, we propose to construct a small sample-oriented deep learning model to assist clinicians in the diagnosis of cervical spine tumors in multi-modal imaging, and to evaluate the benign and malignant tumors. This research will not only improve the efficiency and efficiency of cervical spine tumor imaging diagnosis. Accuracy, to guide clinical personalized treatment, will also provide a basis for the clinical application of deep learning in the field of small samples, which has important clinical significance. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT04959656
Study type Observational
Source Peking University Third Hospital
Contact
Status Completed
Phase
Start date January 1, 2020
Completion date June 1, 2021

See also
  Status Clinical Trial Phase
Active, not recruiting NCT02851706 - Natural History of and Specimen Banking for People With Tumors of the Central Nervous System
Completed NCT01690364 - Comparison of the Effects of Vecuronium and Cisatracurium on Electrophysiologic Monitoring During Neurosurgery N/A
Recruiting NCT05995327 - Reasons and Risk Factors for Unplanned Spinal Re-operation
Completed NCT01643174 - Predictors of Mortality and Morbidity in the Surgical Management of Primary Tumors of the Spine N/A
Recruiting NCT06140927 - Effect of Ketamine on Intraoperative Motor Evoked Potentials Phase 3
Recruiting NCT05519618 - Multi-modality Evaluation of Flow Rate, Pressure and Size of Spine Epidural Venous Plexus