AI-assisted Colonoscopy Report System In Improving Reporting Quality

Colonoscopy Clinical Trial

Official title:

Speech and Image Recognition Based System in Improving Reporting Quality During Colonoscopy

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT05829590
• Other study ID #: EA-23-002
• Status: Not yet recruiting
• Phase: N/A
• Start date: May 15, 2023
• Est. completion date: July 31, 2023

Study information

• Verified date: March 2023
• Source: Renmin Hospital of Wuhan University
• Contact: Honggang Yu, MD
• Phone: 13871281899
• Email: yuhonggang1969[@]163.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

In this study, the investigators proposed a prospective study about the effectiveness of speech and image recognition-based system in improving reporting quality during colonoscopy for colonoscopy report quality in endoscopists. The participants would be divided into two groups. For the collected colonoscopy videos, group A would record their observations with the assistance of the artificial intelligence system. The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions. Group B would complete the endoscopy report without special prompts. After a period of washout period, the two groups switched, that is, group A without AI assistance and group B with AI assistance to complete the colonoscopy report. Then, the completeness of the colonoscopy report, the completeness of capturing anatomical landmarks and detected lesions, the completeness of structured description, the accuracy of lesion reporting, the time for reporting and the satisfaction with the reporting system are compared with or without AI assistance.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 10
• Est. completion date: July 31, 2023
• Est. primary completion date: June 15, 2023
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

Inclusion Criteria:

Patients:

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.

Doctors:

1. Males or females who are over 18 years old;

2. After qualified medical education and obtaining the Physician's Practice License.

Exclusion Criteria:

Patients:

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 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.

Doctors:

1. The researcher believes that the subjects are not suitable for participating in clinical trials.

Related Conditions & MeSH terms

• Artificial Intelligence
• Colonoscopy

Intervention

Diagnostic Test:
• Artificial intelligence assistant system
The artificial intelligence assistant system can automatically capture bowel segment images and prompt abnormal lesions based on speech recognition and deep learning.

Locations

Country	Name	City	State
China	Renmin Hospital of Wuhan Univercity	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
Other	The integrity of report lesion	Report lesion integrity with or without AI-assisted. Calculation method = number of report lesions / total number of lesions x 100%	One month
Other	The completeness of structured description	The completeness of structured description with or without AI-assisted. Calculation method = number of structured descriptions / total number of structured descriptions need to record x 100%	One month
Other	Accuracy of lesion reporting	Accuracy of lesion report with or without AI-assisted. Calculation method = number of lesions with correct description / total number of lesions descriptionx 100%	One month
Other	The time for reporting	The time for reporting with or without AI-assisted	One month
Other	The satisfaction with the reporting system	The satisfaction with the reporting system with or without AI-assisted	One month
Primary	The integrity of colonoscopy report	Report integrity with or without AI-assisted. Calculation method = number of information recorded / total number of information need to record x 100%	One month
Secondary	The integrity of capturing anatomical landmarks	The integrity in captured bowel landmrak images with or without AI-assisted. Calculation method = number of anatomical landmarks in captured images / total number of anatomical landmarks x 100%	One month