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Real-Time Identification System of Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence

Capsule Endoscopy Clinical Trial

Official title:
Real-Time Identification of Gastric Lesions and Anatomical Landmarks During Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence

Clinical Trial Summary

The aim of this study is to evaluate the performance of real-time auxiliary system based on artificial intelligence algorithm in lesion detection and quality control in magnetically controlled capsule endoscopy.

Clinical Trial Description

Magnetically controlled capsule endoscopy (MCE) has been used in clinical practice for gastric examination, with high sensitivity and specificity of 90.4% and 94.7%, respectively.

Therefore, a real-time auxiliary system based on convolutional neural network deep learning framework was developed to assist clinicians to improve the quality in MCE examinations.

Patients referred for magnetically controlled capsule endoscopy (MCE) in the participating center were prospectively enrolled. After passage through the esophagus, physician will finish the gastric examination under magnetic steering with the real-time auxiliary system. Professional operators guarantee the integrity of the examination and the diagnostic results of professional endoscopist was used as the gold standard. The system diagnosis results was recorded at the same time. The sensitivity, delay time, specificity of lesions and anatomical landmarks will be analyzed. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04203264
Study type Observational
Source Changhai Hospital
Contact Zhuan Liao
Phone 008621-31161004
Email liaozhuan@smmu.edu.cn
Status Recruiting
Phase
Start date December 16, 2019
Completion date January 1, 2020
View Details
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