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Trial record 40 of 6681 for:    Recruiting, Not yet recruiting, Available Studies | Digestion

A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy

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ClinicalTrials.gov Identifier: NCT04071678
Recruitment Status : Recruiting
First Posted : August 28, 2019
Last Update Posted : August 30, 2019
Sponsor:
Information provided by (Responsible Party):
Second Affiliated Hospital, School of Medicine, Zhejiang University

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.

Condition or disease Intervention/treatment
Artificial Intelligence Behavioral: Careful examination during endoscopic procedures to identify lesions

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.

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Study Type : Observational
Estimated Enrollment : 3600 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: A Randomized Controlled Multicenter Study of Artificial Intelligence Assisted Digestive Endoscopy
Actual Study Start Date : August 1, 2019
Estimated Primary Completion Date : August 1, 2021
Estimated Study Completion Date : December 30, 2021

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Endoscopy

Group/Cohort Intervention/treatment
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
Behavioral: Careful examination during endoscopic procedures to identify lesions
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
Behavioral: Careful examination during endoscopic procedures to identify lesions
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.
Behavioral: Careful examination during endoscopic procedures to identify lesions
When the AI model alarms, check carefully to confirm the lesion




Primary Outcome Measures :
  1. Changes of detection rate of digestive tract lesions assisted by artificial intelligence gastroenteroscopy [ Time Frame: 2 years ]
    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. The accuracy of AI-assisted diagnostic model evaluating the intestinal readiness score [ Time Frame: 2 years ]
    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.



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Probability Sample
Study Population
Patients who underwent painless gastroenteroscopy at the endoscopy center from September 2019 to August 2021
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04071678


Contacts
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Contact: Wang J An, Dr 057187783759 ext 057187783759 HREC2013@126.com
Contact: Cai J Ting, Dr 15267019902 ext 15267019902 1173920428@qq.com

Locations
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China, Zhejiang
Cai J Ting Recruiting
Hangzhou, Zhejiang, China, 310000
Contact: Cai J Ting    15267019902 ext 15267019902    1173920428@qq.com   
Sponsors and Collaborators
Second Affiliated Hospital, School of Medicine, Zhejiang University
Investigators
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Study Director: Cai J Ting, Dr Second affiliated hospital of school of medicine, zhejiang university

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Responsible Party: Second Affiliated Hospital, School of Medicine, Zhejiang University
ClinicalTrials.gov Identifier: NCT04071678     History of Changes
Other Study ID Numbers: 研2018-218
First Posted: August 28, 2019    Key Record Dates
Last Update Posted: August 30, 2019
Last Verified: August 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: The IPD will not share to others

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No