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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04203264
Other study ID # 20190411A
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date December 16, 2019
Est. completion date January 1, 2020

Study information

Verified date December 2019
Source Changhai Hospital
Contact Zhuan Liao
Phone 008621-31161004
Email liaozhuan@smmu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

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.


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.


Recruitment information / eligibility

Status Recruiting
Enrollment 50
Est. completion date January 1, 2020
Est. primary completion date January 1, 2020
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years to 80 Years
Eligibility Inclusion Criteria:

- With or without gastrointestinal complaints

- Scheduled to undergo a capsule endoscopy for both stomach and small bowel

- Signed the informed consents before joining this study

Exclusion Criteria:

- Dysphagia or symptoms of gastric outlet obstruction, suspected or known intestinal stenosis, overt gastrointestinal bleeding, history of upper gastrointestinal surgery or abdominal surgery altering gastrointestinal anatomy

- Refused abdominal surgery to take out the capsule in case of capsule retention

- Implanted pacemaker, except the pacemaker is compatible with MRI

- Other implanted electromedical devices or magnetic metal foreign bodies

- Pregnancy or suspected pregnancy

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
Real-time artificial intelligence identification system
The patients swallowed the MCE with a small amount of water in the left lateral decubitus position. Once the capsule reached the stomach after investigating the esophagus, it was lifted away from the posterior wall, rotated and advanced to the fundus and cardiac regions, and then to the gastric body, angulus, antrum and pylorus. The magnetic steering time for passing through the pylorus was not allowed more than 15 min. The real-time auxiliary system implemented real-time processing for the output image of MCE 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.

Locations

Country Name City State
China Changhai Hospital Shanghai

Sponsors (1)

Lead Sponsor Collaborator
Changhai Hospital

Country where clinical trial is conducted

China, 

References & Publications (4)

Ding Z, Shi H, Zhang H, Meng L, Fan M, Han C, Zhang K, Ming F, Xie X, Liu H, Liu J, Lin R, Hou X. Gastroenterologist-Level Identification of Small-Bowel Diseases and Normal Variants by Capsule Endoscopy Using a Deep-Learning Model. Gastroenterology. 2019 Oct;157(4):1044-1054.e5. doi: 10.1053/j.gastro.2019.06.025. Epub 2019 Jun 25. — View Citation

Liao Z, Duan XD, Xin L, Bo LM, Wang XH, Xiao GH, Hu LH, Zhuang SL, Li ZS. Feasibility and safety of magnetic-controlled capsule endoscopy system in examination of human stomach: a pilot study in healthy volunteers. J Interv Gastroenterol. 2012 Oct-Dec;2(4):155-160. doi: 10.4161/jig.23751. Epub 2012 Oct 1. — View Citation

Liao Z, Hou X, Lin-Hu EQ, Sheng JQ, Ge ZZ, Jiang B, Hou XH, Liu JY, Li Z, Huang QY, Zhao XJ, Li N, Gao YJ, Zhang Y, Zhou JQ, Wang XY, Liu J, Xie XP, Yang CM, Liu HL, Sun XT, Zou WB, Li ZS. Accuracy of Magnetically Controlled Capsule Endoscopy, Compared With Conventional Gastroscopy, in Detection of Gastric Diseases. Clin Gastroenterol Hepatol. 2016 Sep;14(9):1266-1273.e1. doi: 10.1016/j.cgh.2016.05.013. Epub 2016 May 20. — View Citation

Wu L, Zhang J, Zhou W, An P, Shen L, Liu J, Jiang X, Huang X, Mu G, Wan X, Lv X, Gao J, Cui N, Hu S, Chen Y, Hu X, Li J, Chen D, Gong D, He X, Ding Q, Zhu X, Li S, Wei X, Li X, Wang X, Zhou J, Zhang M, Yu HG. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut. 2019 Dec;68(12):2161-2169. doi: 10.1136/gutjnl-2018-317366. Epub 2019 Mar 11. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary Sensitivity The sensitivity of lesions detected by system up to 2 weeks
Secondary Delay time Average preprocessing and displaying times before and after the execution of system up to 2 weeks
Secondary Specificity The specificity of detected by system up to 2 weeks
Secondary Accuracy of anatomic landmarks identification Accuracy of anatomic landmarks ( cardia, fundus, body, lesser curvature, greater curvature, angle, antrum and pylorus) identified by real-time identification system up to 2 weeks
Secondary Completeness of real-time observation with assistance Whether the clinician observed all anatomic landmarks of stomach (cardia, fundus, body, angulus, antrum and pylorus). up to 2 weeks
Secondary Accuracy of heat map The rate of the highlighted area indicated in the lesion up to 2 weeks
Secondary Lesion detection yield The ratio of lesions detected by system to all lesions up to 2 weeks
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