Clinical trial NCT04071678: A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
Official title: A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Artificial Intelligence
Lead sponsor: Second Affiliated Hospital, School of Medicine, Zhejiang University
Sponsor class: OTHER
First posted: 2019-08-28
Enrollment: 3600
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients who underwent painless gastroenteroscopy at the endoscopy center from September 2019 to August 2021

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy
timeFrame: 2 years
measure: The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score
timeFrame: 2 years
Secondary outcomes:
Other outcomes:
Locations:
city: Hangzhou
country: China
facility: Cai J Ting
state: Zhejiang
Study officials:
affiliation: Second affiliated hospital of school of medicine, zhejiang university
name: Cai J Ting, Dr
role: STUDY_DIRECTOR
References: