Clinical trial NCT04912037: A Study on the Effectiveness of AI-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
Official title: A Study on the Effectiveness of Artificial Intelligence-assisted Colonoscopy in Improving the Effect of Colonoscopy Training for Trainees
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Colonoscopy
Artificial Intelligence
Gastrointestinal Disease
Lead sponsor: Renmin Hospital of Wuhan University
Sponsor class: OTHER
First posted: 2021-06-03
Enrollment: 385
Sex: ALL
Minimum age (years): 50
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

Brief 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。

Detailed 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.

Eligibility criteria: 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.

Primary outcomes:
measure: CUSUM learning curve for colonoscopy (ACE scoring scale)
timeFrame: From the beginning to the end of colonoscopy training
measure: Average test score difference before and after training
timeFrame: From the beginning to the end of colonoscopy training
Secondary outcomes:
measure: Detection rate of advanced adenoma
timeFrame: A month
measure: Polyp Detection Rate, PDR
timeFrame: A month
measure: Average number of adenomas detected per patient
timeFrame: A month
measure: The detection rate of large, small and micro polyps
timeFrame: A month
measure: The average number of large, small and micro polyps detected
timeFrame: A month
measure: The detection rate of large, small and micro adenomas
timeFrame: A month
measure: The average number of large, small and micro adenomas detected
timeFrame: A month
measure: The detection rate of adenoma in different sites
timeFrame: A month
measure: The average number of adenomas detected in different sites
timeFrame: A month
measure: Number of missed return of the sliding endoscopy/number of successful return of the sliding endoscopy
timeFrame: A month
measure: Real-time gut cleanliness score
timeFrame: During procedure
measure: withdraw overspeed percentage
timeFrame: During procedure
measure: The withdraw time
timeFrame: During procedure
measure: Ratio of ileocecal reach
timeFrame: A month
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Renmin hospital of Wuhan University
state: Hubei
Study officials:
affiliation: Renmin Hospital of Wuhan University
name: Yu w Honggang, Doctor
role: PRINCIPAL_INVESTIGATOR
References: