ClinicalTrials.Veeva

Menu

Deep-Learning for Automatic Polyp Detection During Colonoscopy

NYU Langone Health logo

NYU Langone Health

Status

Completed

Conditions

Screening Colonoscopy

Treatments

Device: Computer Algorithm

Study type

Interventional

Funder types

Other

Identifiers

NCT03637712
18-00746

Details and patient eligibility

About

The primary objective of this study is to examine the role of machine learning and computer aided diagnostics in automatic polyp detection and to determine whether a combination of colonoscopy and an automatic polyp detection software is a feasible way to increase adenoma detection rate compared to standard colonoscopy.

Enrollment

5 patients

Sex

All

Ages

18 to 99 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Patients presenting for routine colonoscopy for screening and/or surveillance purposes.
  • Ability to provide written, informed consent and understand the responsibilities of trial participation

Exclusion criteria

  • People with diminished cognitive capacity.
  • 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.

Trial design

Primary purpose

Diagnostic

Allocation

N/A

Interventional model

Single Group Assignment

Masking

None (Open label)

5 participants in 1 patient group

Screening Colonoscopy
Experimental group
Description:
Patients undergoing standard screening or surveillance colonoscopy will be included
Treatment:
Device: Computer Algorithm

Trial contacts and locations

1

Loading...

Data sourced from clinicaltrials.gov

Clinical trials

Find clinical trialsTrials by location
© Copyright 2024 Veeva Systems