Development and Validation of an Artificial Intelligence-assisted Strategy Selection System for Colonoscopy Cleaning
NCT ID: NCT04444908
Last Updated: 2020-11-17
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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UNKNOWN
657 participants
OBSERVATIONAL
2020-05-11
2020-11-16
Brief Summary
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Detailed Description
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Colonoscopy is the simplest and most widely available screening procedure for colorectal cancer(CRC) prevention and early detection. Colonoscopy can clearly observe the small changes in the terminal ileum and the colorectal, such as erosion, ulcers, bleeding, congestion, edema, polyps, early cancer, and so on. Colonoscopy can biopsy the lesion site for pathological examination, to histologically qualitative the characterization of mucosal lesions, such as inflammation, polyp nature, the degree of differentiation of cancer, and so on. It is helpful to understand the severity of the lesion and guide the formulation of the correct treatment plan or judgment of treatment effect. Colonoscopy can also be the minimally invasive endoscopic treatment of colorectal polyps, early cancer, bleeding, foreign bodies and other diseases.
Because the quality of bowel preparation affects the colonoscopy's ability to detect adenomas and polyps, adequate bowel preparation is necessary to ensure optimal use of colonoscopy in CRC prevention. Almost all clinical guidelines recommend adequate bowel preparation before colonoscopy. However, up to one third of colonoscopies have been found to show inadequate bowel preparation, which is estimated to increase the cost of colonoscopies by 12% to 22%. And there are 20% of patients' bowel is not adequately prepared. When the patient's bowel preparation is inadequate, the difficulty of flushing may lead to missed detection of adenomas. so doctors need to accurately identify such patients and tell them to have a second colonoscopy after a full bowel cleanse. However, the evaluation of intestinal cleanliness is decided by doctors subjectively, and there is no objective and effective scoring standard to guide the patients to accept the second colonoscopy.
Deep learning is an important breakthrough in the field of artificial intelligence in the past decade. It has great potential in extracting tiny features in image analysis and image classification. In 2017, the journal Nature published a paper showing that using artificial intelligence to diagnose skin diseases can reach the level of experts. Subsequently, in the field of digestive endoscopy, more and more studies began to apply artificial intelligence to assist doctors to find polyps and improve the detection rate of polyps and adenomas.Urban, G. team used artificial intelligence to identify polyps with 95% sensitivity. Misawa, M team used artificial intelligence to identify polyps with 90% sensitivity. The purpose of our research group is to develop the EndoAngel with real-time intestinal cleanliness assessment. It can derive a decision curve for bowel cleanliness based on the relationship between the percentage of bowel segments with a Boston score of 0-1 and the detection rate of adenomas. It can help endoscopists to identify patients who need a second colonoscopy, to avoid the missed adenomas and the high cost of cleaning time caused by the wrong decision-making. At the same time, artificial intelligence is in the preliminary research stage in the field of digestive endoscopy, our research results are expected to provide new ideas in improving the detection rate of colonoscopic adenomas.
The study Process is: Subjects who met all inclusion criteria and did not meet all exclusion criteria were included in the study before colonoscopy. During the colonoscopy, the endoscopists need to remain in the same without withdrawal while flushing the bowel. The biopsied patients are followed up for one week. the non-biopsied patients are followed up at the end of their colonoscopy , and the results are sent to an independent data analysis team for review. We will collect the patients' video and exclude the clips of irrigation, biopsy, and observation of polyp. Then the EndoAngel evaluates the Boston Bowel Preparation Scale of the ascending colon, transverse colon and descending colon, and calculates the proportion of 1 Score.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* Colonoscopy is needed to further clarify the characteristics of digestive tract diseases;
* Patients able to give informed consent were eligible to participate.
* Able and willing to comply with all study process.
* No intestinal organic disease.
Exclusion Criteria
* Has participated in clinical trials of the drug and is in the elution period of the experimental drug or control drug.
* Drug or alcohol abuse or psychological disorder in the last 5 years.
* Patients in pregnancy or lactation.
* Known polyposis syndromes.
* Gastrointestinal Bleeding.
* A history of inflammatory bowel disease, colorectal cancer and colorectal surgery.
* A history of colorectal surgery.
* Patients with a contraindication for biopsy.
* Previous history of allergy to ingredient of bowel cleanser.
* Patients with intestinal obstruction or perforation, toxic megacolon, Colectomy, heart failure (Grade III or IV) , severe cardiovascular disease, severe liver failure or renal insufficiency, etc. .
* Researchers believe that the patient is not suitable to participate in the trial.
18 Years
ALL
No
Sponsors
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Renmin Hospital of Wuhan University
OTHER
Responsible Party
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Principal Investigators
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Honggang Yu, Doctor
Role: PRINCIPAL_INVESTIGATOR
Renmin Hospital of Wuhan University
Locations
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Renmin hospital
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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Honggang Yu, Doctor
Role: primary
Huiling Wu, Doctor
Role: backup
Provided Documents
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Document Type: Study Protocol, Statistical Analysis Plan, and Informed Consent Form
Other Identifiers
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WDRY2019-KK073
Identifier Type: -
Identifier Source: org_study_id