Colonic Polyps Clinical Trial
Official title:
Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
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 |
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.
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 |
Country | Name | City | State |
---|---|---|---|
China | Changhai Hospital, Second Military Medical University | Shanghai |
Lead Sponsor | Collaborator |
---|---|
Changhai Hospital | The First Affiliated Hospital of Dalian Medical University, Wenzhou Central Hospital, Wuhan Union Hospital, China |
China,
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
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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