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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT03967756
Other study ID # AI-2
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
Start date June 1, 2019
Est. completion date October 1, 2021

Study information

Verified date April 2021
Source Changhai Hospital
Contact Zhaoshen Li, M.D
Phone 86-21-31161365
Email li.zhaoshen@hotmail.com
Is FDA regulated No
Health authority
Study type Interventional

Clinical Trial Summary

In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.


Recruitment information / eligibility

Status Recruiting
Enrollment 1118
Est. completion date October 1, 2021
Est. primary completion date July 20, 2021
Accepts healthy volunteers No
Gender All
Age group 40 Years to 85 Years
Eligibility Inclusion Criteria: - Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic. - Patients who have signed inform consent form. Exclusion Criteria: - Patients who have undergone colonic resection - Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage. - Patients with severe chronic cardiopulmonary and renal disease. - Patients who are unwilling or unable to consent. - Patients who are not suitable for colonoscopy - Patients who received urgent or therapeutic colonoscopy - Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction - Patients who are taking aspirin, clopidogrel or other anticoagulants - Patients with withdrawal time < 6 min

Study Design


Related Conditions & MeSH terms


Intervention

Device:
Automatic polyp detection system
When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system, there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.

Locations

Country Name City State
China Changhai Hospital, Second Military Medical University Shanghai

Sponsors (4)

Lead Sponsor Collaborator
Changhai Hospital The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Wuhan Union Hospital, China

Country where clinical trial is conducted

China, 

References & Publications (2)

Ahmad OF, Soares AS, Mazomenos E, Brandao P, Vega R, Seward E, Stoyanov D, Chand M, Lovat LB. Artificial intelligence and computer-aided diagnosis in colonoscopy: current evidence and future directions. Lancet Gastroenterol Hepatol. 2019 Jan;4(1):71-80. doi: 10.1016/S2468-1253(18)30282-6. Epub 2018 Dec 6. Review. — View Citation

Urban G, Tripathi P, Alkayali T, Mittal M, Jalali F, Karnes W, Baldi P. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. Gastroenterology. 2018 Oct;155(4):1069-1078.e8. doi: 10.1053/j.gastro.2018.06.037. Epub 2018 Jun 18. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary adenoma detection rate(ADR) the number of patients with at least one adenoma divided by the total number of patients. 30 minutes
Secondary polyp detection rate(PDR) the number of patients with at least one polyp divided by the total number of patients. 30 minutes
Secondary adenoma per colonoscopy the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies. 30 minutes
Secondary polyp per colonoscopy the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies. 30 minutes
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