Clinical trial NCT04203264: Real-Time Identification System of Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence
Official title: Real-Time Identification of Gastric Lesions and Anatomical Landmarks During Magnetically Controlled Capsule Endoscopy Using Artificial Intelligence
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Capsule Endoscopy
Keywords: artificial intelligence
magnetically controlled capsule endoscopy
Lead sponsor: Changhai Hospital
Sponsor class: OTHER
First posted: 2019-12-18
Enrollment: 50
Sex: ALL
Minimum age (years): 18
Maximum age (years): 80
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Outpatients undergoing magnetically controlled capsule endoscopy examination

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Sensitivity
timeFrame: up to 2 weeks
Secondary outcomes:
measure: Delay time
timeFrame: up to 2 weeks
measure: Specificity
timeFrame: up to 2 weeks
measure: Accuracy of anatomic landmarks identification
timeFrame: up to 2 weeks
measure: Completeness of real-time observation with assistance
timeFrame: up to 2 weeks
measure: Accuracy of heat map
timeFrame: up to 2 weeks
measure: Lesion detection yield
timeFrame: up to 2 weeks
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Changhai Hospital
Study officials:
affiliation: Changhai Hospital
name: Zhuan Liao
role: STUDY_CHAIR
References:
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.
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.
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citation: 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.