Clinical trial NCT04444908: Development and Validation of an Artificial Intelligence-assisted Strategy Selection System for Colonoscopy Cleaning
Official title: Development and Validation of an Artificial Intelligence-assisted Strategy Selection System for Colonoscopy Cleaning
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Colorectal Adenoma
Lead sponsor: Renmin Hospital of Wuhan University
Sponsor class: OTHER
First posted: 2020-06-24
Enrollment: 657
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The primary endpoint was the detection rate of Adenomas and the percentage of 1 score to get a correlation curve. In order to calculate the sample size more accurately, we prepare to collect 200 colonoscopies to explore the sample distribution, and then calculate the sample size according to the sample distribution.

Brief summary: Patients with poor inadequate bowel preparation need to undergo secondary colonoscopy. but the evaluation of intestinal cleanliness is judged by doctors subjectively. there are no objective and effective criteria to guide the evaluation. We use the deep learning technique 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 1 and the adenoma detection rate. It can help doctors to identify patients who need a second colonoscopy, and provide a new way for artificial intelligence in improving the detection rate of colonoscopic adenomas.

Detailed description: Colorectal disease as a common human disease seriously affects the health of human life. With the aging of the population, the change of diet structure and the aggravation of environmental pollution, the incidence of colorectal diseases, such as colon cancer, Colon Polyp and inflammatory bowel disease, has gradually increased.

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.

Eligibility criteria: Inclusion Criteria:

* Male or female aged 18 or above;
* 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 other clinical trials, signed informed consent and was in the follow-up period of other clinical trials.
* 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.

Primary outcomes:
measure: The adenoma detection rate (ADR)
timeFrame: 2020.08.7
measure: Cleanliness assessment of different intestinal segment in the artificial intelligence system
timeFrame: 2020.08.7
Secondary outcomes:
measure: The polyp detection rate (PDR)
timeFrame: 2020.08.7
measure: The mean number of polyps per patient (MNP)
timeFrame: 2020.08.7
measure: The mean number of adenomas per patient (MAP)
timeFrame: 2020.08.7
measure: PDR of different sizes
timeFrame: 2020.08.7
measure: MNP of different sizes
timeFrame: 2020.08.07
measure: ADR of different sizes
timeFrame: 2020.08.07
measure: MAP of different sizes
timeFrame: 2020.08.07
measure: ADR of different location
timeFrame: 2020.08.07
measure: MAP of different location
timeFrame: 2020.08.07
measure: Scope-forward time and Withdrawal time
timeFrame: 2020.08.07
measure: Boston Bowel Preparation Score of endoscopists
timeFrame: 2020.08.07
measure: Cecal intubation rate
timeFrame: 2020.08.07
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Renmin hospital
state: Hubei
Study officials:
affiliation: Renmin Hospital of Wuhan University
name: Honggang Yu, Doctor
role: PRINCIPAL_INVESTIGATOR
References: