A Real-time Quality Control System of Magnetic-controlled Capsule Gastroscopy

Study Overview
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Brief Summary
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With the rapid development of artificial intelligence technology, more and more deep learning technology has been applied to medicine. Our research is to develop a set of quality control system for magnetic capsule gastroscope using deep learning technology, and conduct a randomized controlled trial to verify its practical efficiency.

Detailed Description
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The accuracy of magnetic controlled capsule endoscopy(MCCE) in the diagnosis of stomach lesions is highly consistent with the traditional electronic gastroscopy. It has become a new comfortable and safe form for screening and It is also the beneficial complementarity of the traditional electronic gastroscope. To ensure the medical quality of magnetic controlled capsule endoscopy system, Based on Artificial Intelligence Deep Learning Technology ,investigators developed the Magnetic-controlled Capsule Endoscopic Assisted Quality Control System(AI-box).Randomized controlled trials will be conducted on prospective subjects to verify the quality control efficiency.

Official Title
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A Prospective Randomized Controlled Trial of AI-box , a Real-time Quality Control System of Magnetic-controlled Capsule Gastroscopy.

Conditions
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Magnet Controlled Capsule Endoscopy

Intervention / Treatment
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* Other: AI-box


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Consecutive patients aged 18-75 years old who underwent MCCG examination in Qilu Hospital of Shandong University, Shandong Provincial Hospital affilated to Shandong First Medican University, and the Binzhou Medical University Hospital. Exclusion Criteria: People who are allergic to the ingredients prepared in the stomach before examination; Patients with astrointestinal bleeding or ulcers, or prior gastrointestinal bleeding or ulcers within the last 24 months; Patients with esophageal and gastric tumor diseases that are undergoing active surgical treatment; Contraindications to the MCE test, including suspected or known gastrointestinal obstruction, stenosis, fistula, diverticula, etc; presence of gastrointestinal obstruction symptoms such as pain or dysphagia; Prior gastrointestinal tract or abdominal surgery other than simple procedures which would not change the gastrointestinal tract anatomy, such as polyp removal, cholecystectomy or appendectomy; Inoperative conditions or refusal to undergo abdominal surgery if required (ie, if the capsule will not pass and cannot be removed by endoscopy); A cardiac pacemaker is installed in the body, except when the pacemaker is a new MRE-compatible product; Electronic devices such as cochlear implant, magnetic metal drug infusion pump, nerve stimulator and magnetic metal foreign body are implanted in vivo; Planned MRI examination before capsule endoscopy discharge; Pregnant women; Patients with deglutition disorders or gastric emptizing disorders.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 75 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Participates being subjected to capsule endoscopy (AI-box assistant)<br>In this group ,Participates will be subjected to magnet controlled capsule endoscopy with AI-box assistant. | Other: AI-box<br>* Automatic quality-control system（AI-box）could real-time measure endoscopic inspection completeness, evaluate gastric cleanliness.<br>|
| No Intervention: Participates being subjected to capsule endoscopy<br>In this group ,Participates will be subjected to magnet controlled capsule endoscopy without AI-box assistant. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Mean inspection completeness | After the inspection，investigators will review the whole process of inspection and record the sites observed as well as unobserved sites，then calculate the inspection completeness（number of observed sites in each patient/10）besides the the blind spot rate(number of unobserved sites in each patient/10 ) per procedure in control group and AI-assisted group. | 4 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cleanliness judgement consistency between expert and AI-box | After the inspection，investigators will review the whole process of inspection and make a judgement of cleanliness of different sites of gastric then compared with the cleanliness judgement of AI-box. So at last investigators will calculate the accuracy of the AI result. | 4 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, Deep learning