Development and Validation of an Artificial Intelligence-assisted Strategy Selection System for Colonoscopy Cleaning

Overview

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.

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: October 15, 2020

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.

Clinical Trial Outcome Measures

Primary Measures

  • The adenoma detection rate (ADR)
    • Time Frame: 2020.08.7
    • ADR was calculated by dividing the total number of patients being detected adenomas by the number of colonoscopies.
  • Cleanliness assessment of different intestinal segment in the artificial intelligence system
    • Time Frame: 2020.08.7
    • The Artificial intelligence evaluates the Boston Bowel Preparation score of the ascending colon, transverse colon and descending colon in real-time, and calculates the proportion of 1 Score

Secondary Measures

  • The polyp detection rate (PDR)
    • Time Frame: 2020.08.7
    • PDR was calculated by dividing the total number of patients being detected polyps by the number of colonoscopies
  • The mean number of polyps per patient (MNP)
    • Time Frame: 2020.08.7
    • MNP was calculated by dividing the total number of polyps by the number of colonoscopies.
  • The mean number of adenomas per patient (MAP)
    • Time Frame: 2020.08.7
    • MAP was calculated by dividing the total number of adenomas by the number of colonoscopies
  • PDR of different sizes
    • Time Frame: 2020.08.7
    • It was calculated by dividing the number of patients with polyps that large (≥10 mm), small (6-9 mm) and diminutive (≤5 mm) by the number of patients undergoing colonoscopy.
  • MNP of different sizes
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of polyps that large (≥10 mm), small (6-9 mm) and diminutive≤5 mm) by the number of patients undergoing colonoscopy.
  • ADR of different sizes
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of patients with adenomas that large (≥10 mm), small (6-9 mm) and diminutive≤5 mm) by the number of patients undergoing colonoscopy.
  • MAP of different sizes
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of adenomas that large (≥10 mm), small (6-9 mm) and diminutive≤5 mm) by the number of patients undergoing colonoscopy.
  • ADR of different location
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of patients with adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region etc. by the Total number of patients undergoing colonoscopy.
  • MAP of different location
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region etc. by the Total number of patients undergoing colonoscopy.
  • Scope-forward time and Withdrawal time
    • Time Frame: 2020.08.07
    • Scope-forward time: The time is taken to go from the the rectum to the ileocecal region. Withdrawal time. The time is taken to finish the examination from the beginning of the ileocecal region.
  • Boston Bowel Preparation Score of endoscopists
    • Time Frame: 2020.08.07
    • Endoscopists evaluate the different intestinal segment according Boston Bowel Preparation Scale(BBPS)
  • Cecal intubation rate
    • Time Frame: 2020.08.07
    • It was calculated by dividing the number of colonoscopies that get to the ileocecal region by the total number of colonoscopies.

Participating in This Clinical Trial

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.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: No

Investigator Details

  • Lead Sponsor
    • Renmin Hospital of Wuhan University
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Honggang Yu, Doctor, Principal Investigator, Renmin Hospital of Wuhan University
  • Overall Contact(s)
    • Honggang Yu, Doctor, 13871281899, yuhonggang@whu.edu.cn

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