A Study on the Effectiveness of AI-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees

Colonoscopy Clinical Trial

Official title:

A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04912037
• Other study ID #: EA-21-005
• Status: Recruiting
• Phase: N/A
• Start date: June 1, 2021
• Est. completion date: February 1, 2022

Study information

• Verified date: May 2021
• Source: Renmin Hospital of Wuhan University
• Contact: Yu W Honggang, Doctor
• Phone: +862788041911
• Email: whdxrmyy[@]126.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

In this study,the AI-assisted system（EndoAngel）has the functions of reminding the ileocecal junction, withdrawal time, withdrawal speed, sliding lens, polyps in the field of vision, etc. These functions can improve the colonoscopy performance of novice physicians and assist the colonoscopy training。

Description:

Colonoscopy is a key technique for detecting and diagnosing lesions of the lower digestive tract.High-quality endoscopy leads to better disease outcomes.However, the demand for endoscopy is high in China, and endoscopy is in short supply.A colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety.Therefore, it is of great significance to improve the colonoscopy ability of novice physicians and shorten the colonoscopy training time for solving the problems such as the lack and uneven distribution of digestive endoscopists and the substandard quality of endoscopy in China. In recent years, deep learning algorithms have been continuously developed and increasingly mature.They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines "see". Through deep learning, camera and computer can replace human eyes to carry out machine vision such as target recognition, tracking and measurement.Interdisciplinary cooperation in the field of medical imaging and computer vision is also one of the research hotspots in recent years. At present, it is mainly applied to the automatic identification and detection of lesions and quality control, and has achieved good results. Our preliminary experiments have shown that deep learning has a high accuracy in endoscopic quality monitoring, which can effectively regulate doctors' operations, reduce blind spots and improve the quality of endoscopic examination.At the same time, it can also monitor the doctor's withdrawal time in real time and improve the detection rate of adenoma.In the previous work of our research group, we have successfully developed deep learning-based colonoscopy withdraw speed monitoring and intestinal cleanliness assessment, and verified the effectiveness of the AI-assisted system（EndoAngel） in improving the quality of gastroscopy and colonoscopy in clinical trials. Based on the above rich foundation of preliminary work, as well as the huge demand in the field of colonoscopy training,By comparing the colonoscopy operation training for novices with and without EndoAngel assistance, we plan to compare the colonoscopy learning effect of novices with and without assistance, including skill results and cognitive level, to explore whether AI can promote the improvement of the colonoscopy operation training for novices.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 385
• Est. completion date: February 1, 2022
• Est. primary completion date: January 1, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 50 Years and older

Eligibility

Inclusion Criteria:

1. Male or female =50 years old;

2. Able to read, understand and sign informed consent

3. The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures

4. Patients requiring colonoscopy

Exclusion Criteria:

1. Have drug or alcohol abuse or mental disorder in the last 5 years

2. Pregnant or lactating women

3. Patients with known multiple polyp syndrome;

4. patients with known inflammatory bowel disease;

5. known intestinal stenosis or space-occupying tumor;

6. known colon obstruction or perforation;

7. patients with a history of colorectal surgery;

8. Patients with previous history of allergy to pre-used spasmolysis;

9. Unable to perform biopsy and polyp removal due to coagulation disorders or oral anticoagulants;

10. High risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Related Conditions & MeSH terms

• Artificial Intelligence
• Colonoscopy
• Digestive System Diseases
• Gastrointestinal Disease
• Gastrointestinal Diseases

Intervention

Device:
• artificial intelligence assistance system
the artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed, and feedback the overspeed percentage.

Locations

Country	Name	City	State
China	Renmin hospital of Wuhan University	Wuhan	Hubei

Sponsors (1)

Lead Sponsor	Collaborator
Renmin Hospital of Wuhan University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	CUSUM learning curve for colonoscopy (ACE scoring scale)		From the beginning to the end of colonoscopy training
Primary	Average test score difference before and after training		From the beginning to the end of colonoscopy training
Secondary	Detection rate of advanced adenoma	The numerator is the number of patients diagnosed with advanced adenomas, and the denominator is the total number of patients undergoing colonoscopy,Advanced adenoma was defined as > 10mm adenoma, villous adenoma, tubular villous adenoma, high-grade intraepithelial neoplasia, and carcinoma.	A month
Secondary	Polyp Detection Rate, PDR	The numerator is the number of patients with polyps detected by colonoscopy, and the denominator is the total number of patients who underwent colonoscopy	A month
Secondary	Average number of adenomas detected per patient	The numerator is the total number of adenomas detected by colonoscopy, and the denominator is the total number of patients undergoing colonoscopy.	A month
Secondary	The detection rate of large, small and micro polyps	The numerator is the number of patients with large (=10 mm), small (6-9 mm) and micro-small (=5 mm) polyps detected by colonoscopy, and the denominator is the total number of patients receiving colonoscopy.	A month
Secondary	The average number of large, small and micro polyps detected	The numerator is the total number of large (=10 mm), small (6-9 mm) and micro-small (=5 mm) polyps detected by colonoscopy, and denominator is the total number of patients undergoing colonoscopy.	A month
Secondary	The detection rate of large, small and micro adenomas	The numerator is the number of patients with large (=10 mm), small (6-9 mm) and micro-small (=5 mm) adenomas detected by colonoscopy, and the denominator is the total number of patients receiving colonoscopy.	A month
Secondary	The average number of large, small and micro adenomas detected	The numerator is the total number of large (=10 mm), small (6-9 mm) and micro-small (=5 mm) adenomas detected by colonoscopy, and denominator is the total number of patients undergoing colonoscopy.	A month
Secondary	The detection rate of adenoma in different sites	The numerator is the number of patients with adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region and other sites during colonoscopy, and the denominator is the total number of patients receiving colonoscopy.	A month
Secondary	The average number of adenomas detected in different sites	The numerator is the total number of adenomas detected in the rectum, sigmoid colon, descending colon, transverse colon, ascending colon, ileocecal region and other sites during colonoscopy, and the denominator is the total number of patients undergoing colonoscopy.	A month
Secondary	Number of missed return of the sliding endoscopy/number of successful return of the sliding endoscopy	The numerator is the total number of sliding endoscopy during colonoscopy, and the denominator is the number of sliding endoscopy and successful return endoscopy during colonoscopy	A month
Secondary	Real-time gut cleanliness score	During colonoscopy, a real-time intestinal cleanliness score was given by EndoAngel based on the Boston-scale Boreal Preparation Score (BBPS).	During procedure
Secondary	withdraw overspeed percentage	The ratio of the overspeed duration to the total duration in the process of withdrawal.	During procedure
Secondary	The withdraw time	The time between colonoscopy arrival at ileocecal valve and colonoscopy exit from anus.	During procedure
Secondary	Ratio of ileocecal reach	For a period of time, the number of colonoscopies that failed to reach the ileocecal part accounted for the proportion of the total number of colonoscopies.	A month