Clinical trial NCT03967756: Impact of Automatic Polyp Detection System on Adenoma Detection Rate
Official title: Impact of Automatic Polyp Detection System on Adenoma Detection Rate-a Multicenter,Prospective, Randomized Controlled Trial
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Colonic Polyps
Colorectal Adenomas
Lead sponsor: Changhai Hospital
Sponsor class: OTHER
Collaborators: name: The First Affiliated Hospital of Dalian Medical University
name: Wenzhou Central Hospital
name: Wuhan Union Hospital, China
First posted: 2019-05-30
Enrollment: 1118
Sex: ALL
Minimum age (years): 40
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

Brief 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.


Eligibility criteria: 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

Primary outcomes:
measure: adenoma detection rate(ADR)
timeFrame: 30 minutes
Secondary outcomes:
measure: polyp detection rate(PDR)
timeFrame: 30 minutes
measure: adenoma per colonoscopy
timeFrame: 30 minutes
measure: polyp per colonoscopy
timeFrame: 30 minutes
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Changhai Hospital, Second Military Medical University
Study officials:
affiliation: Changhai Hospital
name: Zhaoshen Li, M.D
role: PRINCIPAL_INVESTIGATOR
References:
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.
citation: 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.