A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

Artificial Intelligence Clinical Trial

Official title:

A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04071678
• Other study ID #: ?2019-262
• Status: Recruiting
• Start date: August 1, 2019
• Est. completion date: December 30, 2021

Study information

• Verified date: August 2019
• Source: Second Affiliated Hospital, School of Medicine, Zhejiang University
• Contact: Wang J An, Dr
• Phone: 057187783759
• Email: HREC2013[@]126.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model.The deep learning model through the early stage of the study, is able to identify lesions of digest tract.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.

Description:

Digestive endoscopy center of the second affiliated hospital of medical college of zhejiang university and engineers of naki medical co., ltd. in Hong Kong independently developed an ai-assisted diagnostic model of digestive endoscopy in the early stage, namely the deep learning model。The deep learning model through the early stage of the study, is able to identify lesions of colon polyps, colorectal cancer, colorectal apophysis lesions, colonic diverticulum, ulcerative colitis, gastric ulcer, gastric polyps, submucosal uplift, reflux esophagitis, esophageal ulcer, esophageal polyp, esophageal erosion, esophageal ectopic gastric mucosa and esophagus varicosity, esophageal cancer, esophageal papilloma, etc.The sensitivity for the diagnosis of some diseases, such as colon polyps, is 99%. On the one hand, this auxiliary diagnostic model can guide endoscopic examination for beginners; on the other hand, it can improve the detection rate of lesions and reduce the rate of missed diagnosis; on the other hand, the overall operating efficiency of the endoscopic center is improved, which is conducive to the quality control of endoscopic examination. Now the AI-assisted diagnostic model has been further improved, and it is planned to carry out further clinical verification in the digestive endoscopy center of our hospital. It is connected to the endoscopic system of our hospital and used simultaneously with the existing image-text system of endoscopy to compare the practicability, sensitivity and specificity of AI-assisted diagnosis model in the diagnosis of digestive tract diseases, and focus on the quality control of endoscopic examination.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 3600
• Est. completion date: December 30, 2021
• Est. primary completion date: August 1, 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Voluntarily sign the informed consent for this study

• Stable vital signs

• Over 18 years old

• Patients requiring painless gastroenteroscopy for various reasons

Exclusion Criteria:

• Unable or unwilling to sign a consent form, or unable to follow research procedures

• have contraindications to painless gastroenteroscopy

• Vital signs are unstable

• The lesions have been identified by gastroenteroscopy in other hospitals, which is to further confirm the patients who come to our hospital for endoscopic examination

• Endoscopic treatment, such as polypectomy, pylorus narrow dilatation and so on

Related Conditions & MeSH terms

• Artificial Intelligence

Intervention

Behavioral:
• Careful examination during endoscopic procedures to identify lesions
When the AI model alarms, check carefully to confirm the lesion

Locations

Country	Name	City	State
China	Cai J Ting	Hangzhou	Zhejiang

Sponsors (1)

Lead Sponsor	Collaborator
Second Affiliated Hospital, School of Medicine, Zhejiang University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy	Endoscopic examination has a high dependence on the clinical experience and status of endoscopists, and the quality of endoscopic examination of endoscopists can be reduced by high-load work, and problems such as incomplete examination site coverage, incomplete detection of lesions, and incomplete image collection are easy to occur. Artificial intelligence does not have this weakness. It does not reduce its ability to work over a long period of time, and its assistance is expected to improve the detection rate of lesions	2 years
Primary	The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score	The quality of intestinal preparation determines the quality of colonoscopy, which is evaluated by endoscopists through the Boston score. The ai-assisted diagnostic model can also be automatically graded.The Boston bowel score is used to determine whether the bowel is adequately prepared. The Boston bowel score is divided into 4 grades (0~3 points) from worst to cleanest. The higher the score is, the better the bowel is prepared and more conducive to colonoscopy.	2 years