Artificial Intelligence, Colonoscopy, Deep Learning, Gastrointestinal Disease
Conditions
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 assist novice endoscopists in performing colonoscopy and improve the quality.
Detailed description
Colonoscopy is a crucial technique for detecting and diagnosing lower digestive tract lesions. The demand for endoscopy is high in China, and endoscopy is in short supply. However, a colonoscopy is a complex technical procedure that requires training and experience for maximal accuracy and safety. The ability of different endoscopists varies greatly. Novice endoscopists generally have difficulty and high risk in entering colonoscopy, requiring experts' assistance. To some extent, this wastes the novice's productivity. If investigators can arrange the working mode of experts entering and novices withdrawing endoscopy, the clinical efficiency and resource utilization rate can be significantly improved. However, investigators must consider the poor examination ability of novice endoscopists. It is reported that the detection rate of adenoma in colonoscopy performed by endoscopists with different seniority is 7.4% \ 52.5%. If the examination ability of novice endoscopists can be improved, this concern can be eliminated. Deep learning algorithms have been continuously developed and increasingly mature in recent years. They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines to 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 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. Investigator's preliminary experiments have shown that deep learning has 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 investigator's research group, investigators 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 and the massive demand for improving the colonoscopy ability of novices. By comparing the performance of novices and novices with EndoAngel assistance and experts in colonoscopy, investigators want to explore whether artificial intelligence can assist novices to reach the expert level in colonoscopy.
Interventions
The artificial intelligence assistance system can indicate abnormal lesions and real-time withdrawal speed and feedback the overspeed percentage.
Sponsors
Renmin Hospital of Wuhan University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
SINGLE (Investigator)
Masking description
Double (Participant, Investigator)
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Male or female ≥18 years old; 2. Able to read, understand and sign an 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 a 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Missed diagnosis rate of adenoma | A month | The number of newly detected adenoma in the second examination divided by the total number of adenoma detected in both examinations |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Polyp Detection Rate | A month | The numerator is the number of patients with polyps detected by colonoscopy, and the denominator is the total number of patients who underwent colonoscopy |
| Average number of adenomas detected per patient | A month | The numerator is the total number of adenomas detected by colonoscopy, and the denominator is the total number of patients undergoing colonoscopy. |
| The detection rate of large, small and micro polyps | A month | 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. |
| The average number of large, small and micro polyps detected | A month | 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. |
| Detection rate of advanced adenoma | A month | 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, villous adenoma, tubular villous adenoma, high-grade intraepithelial neoplasia, and carcinoma. |
| The average number of large, small and micro adenomas detected | A month | The numerator is the total number of 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 undergoing colonoscopy. |
| The detection rate of adenoma in different sites | A month | 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. |
| The average number of adenomas detected in different sites | A month | 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. |
| Detection rate of adenoma | A month | The numerator is the number of patients diagnosed with adenomas, and the denominator is the total number of patients undergoing colonoscopy. |
| The detection rate of large, small and micro adenomas | A month | 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. |
Countries
China
Outcome results
None listed