A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

3600 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-08-01

Study Completion Date

2021-12-30

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Artificial Intelligence

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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A: Model A

Mode A was silent mode, back-to-back with endoscopic physicians to simultaneously display endoscopic images and record video, but did not interfere with the operation of endoscopic physicians.After the operation, the AI model automatically generates an endoscopy report, which is compared with the official report given by the endoscopy doctor in the endoscopy system. If the difference is large, video verification shall be played back immediately or endoscopic examination shall be performed again before the patient wakes up

Careful examination during endoscopic procedures to identify lesions

Intervention Type BEHAVIORAL

When the AI model alarms, check carefully to confirm the lesion

B: Model B

Mode B is a delayed reminder mode. If the lesion is found during the operation, it is required to be moved to the middle of the visual field within 5 seconds. If the lesion has been detected by the AI model (the lesion has been circled in the picture), but the doctor does not move the lesion to the middle of the visual field within 5 seconds, the AI system will give an alarm prompt

Careful examination during endoscopic procedures to identify lesions

Intervention Type BEHAVIORAL

When the AI model alarms, check carefully to confirm the lesion

C: Model C

Mode C is a real-time reminder mode, which is an alarm prompt when the focus is captured in the visual field.

Careful examination during endoscopic procedures to identify lesions

Intervention Type BEHAVIORAL

When the AI model alarms, check carefully to confirm the lesion

Interventions

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Careful examination during endoscopic procedures to identify lesions

When the AI model alarms, check carefully to confirm the lesion

Intervention Type BEHAVIORAL

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No