Impact of Automatic Polyp Detection System on Adenoma Detection Rate
Study Details
Study Description
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.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
N/A |
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Experimental: AI-assisted withdrawal group A deep learning-based automatic polyp detection system was used to assist the endoscopist. |
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.
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| No Intervention: Routine withdrawal group Routine withdrawal without any assist. |
Outcome Measures
Primary Outcome Measures
- adenoma detection rate(ADR) [30 minutes]
the number of patients with at least one adenoma divided by the total number of patients.
Secondary Outcome Measures
- polyp detection rate(PDR) [30 minutes]
the number of patients with at least one polyp divided by the total number of patients.
- adenoma per colonoscopy [30 minutes]
the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
- polyp per colonoscopy [30 minutes]
the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Patients aged between 40-85 years old who have indications for screening, surveillance and diagnostic.
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Patients who have signed inform consent form.
Exclusion Criteria:
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Patients who have undergone colonic resection
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Patients with intracranial and/or central nervous system disease, including cerebral infarction and cerebral hemorrhage.
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Patients with severe chronic cardiopulmonary and renal disease.
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Patients who are unwilling or unable to consent.
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Patients who are not suitable for colonoscopy
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Patients who received urgent or therapeutic colonoscopy
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Patients with pregnancy, inflammatory bowel disease, polyposis of colon, colorectal cancer, or intestinal obstruction
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Patients who are taking aspirin, clopidogrel or other anticoagulants
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Patients with withdrawal time < 6 min
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Changhai Hospital, Second Military Medical University | Shanghai | China | 200433 |
Sponsors and Collaborators
- Changhai Hospital
- The First Affiliated Hospital of Dalian Medical University
- Wenzhou Central Hospital
- Wuhan Union Hospital, China
Investigators
- Principal Investigator: Zhaoshen Li, M.D, Changhai Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
- 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.
- 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.
- AI-2