Deep-Learning for Automatic Polyp Detection During Colonoscopy
NCT ID: NCT03637712
Last Updated: 2020-05-15
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.
COMPLETED
NA
5 participants
INTERVENTIONAL
2018-09-01
2019-07-07
Brief Summary
Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.
Related Clinical Trials
Explore similar clinical trials based on study characteristics and research focus.
Interest of Artificial Intelligence in Cancer Screening Colonoscopy
NCT04921488
Artificial Intelligence in Colonoscopy
NCT06786793
A Study Comparing Standard and AI-Assisted Colonoscopies for Detecting and Characterizing Colorectal Lesions in Adults Aged 50-74 Undergoing Cancer Screening
NCT07125300
Artificial Intelligence-Assisted Colonoscopy in Colorectal Cancer Screening in a General Hospital
NCT06792292
Polyp Measurement Device
NCT03856255
Detailed Description
Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.
Conditions
See the medical conditions and disease areas that this research is targeting or investigating.
Study Design
Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.
NA
SINGLE_GROUP
DIAGNOSTIC
NONE
Study Groups
Review each arm or cohort in the study, along with the interventions and objectives associated with them.
Screening Colonoscopy
Patients undergoing standard screening or surveillance colonoscopy will be included
Computer Algorithm
This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps
Interventions
Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.
Computer Algorithm
This device is a computer algorithm that runs in the background during routine screening or surveillance colonoscopy that is designed to aid in the detection of polyps
Eligibility Criteria
Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.
Inclusion Criteria
* Ability to provide written, informed consent and understand the responsibilities of trial participation
Exclusion Criteria
* The subject is pregnant or planning a pregnancy during the study period.
* Patients undergoing diagnostic colonoscopy (e.g. as an evaluation for active GI bleed)
* Patients with incomplete colonoscopies (those where endoscopists did not successfully intubate the cecum due to technical difficulties or poor bowel preparation)
* Patients that have standard contraindications to colonoscopy in general (e.g. documented acute diverticulitis, fulminant colitis and known or suspected perforation).
* Patients with inflammatory bowel disease
* Patients with any polypoid/ulcerated lesion \> 20mm concerning for invasive cancer on endoscopy.
18 Years
99 Years
ALL
No
Sponsors
Meet the organizations funding or collaborating on the study and learn about their roles.
NYU Langone Health
OTHER
Responsible Party
Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.
Principal Investigators
Learn about the lead researchers overseeing the trial and their institutional affiliations.
Seth Gross, MD
Role: PRINCIPAL_INVESTIGATOR
NYU Langone Health
Locations
Explore where the study is taking place and check the recruitment status at each participating site.
NYU Langone Health
New York, New York, United States
Countries
Review the countries where the study has at least one active or historical site.
Other Identifiers
Review additional registry numbers or institutional identifiers associated with this trial.
18-00746
Identifier Type: -
Identifier Source: org_study_id
More Related Trials
Additional clinical trials that may be relevant based on similarity analysis.