Polyp Artificial Intelligence Study

NCT04425941 | Completed

• 1 other identifier: PetzACTHospital
• Study Type: observational
• Target: 373
• Locations: 0 countries
• Sites: N/A

Brief Summary

Background We are developing artificial intelligence based polyp histology prediction (AIPHP) method to automatically classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the non-neoplastic or neoplastic histology of polyps. Aim Our aim was to analyse the accuracy of AIPHP and NICE classification based histology predictions and also to compare the results of the two methods. Methods We examined colorectal polyps obtained from colonoscopy patients who had polypectomy or endoscopic mucosectomy. Polyps detected by white light colonoscopy were observed then by using NBI at the optical maximum magnificent (60x). The obtained and stored NBI magnifying images were analysed by NICE classification and by AIPHP method parallelly. Pathology examinations were performed blinded to the NICE and AIPHP diagnosis, as well. Our AIPHP software is based on a machine learning method. This program measures five geometrical and colour features on the endoscopic image.

Conditions

Software Analysis on Polyp Histology Prediction

Outcome Measures

Primary Outcomes (1)

• Software accuracy of polyp histology prediction

  Artificial intelligence software diagnosis in comparison with the polyp histology

  2014-2020

Interventions

artificial intelligence diagnosis OTHER

artificial intelligence prediction of colorectal polyp histology

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients with symptoms of colorectal polypoid laesions, colorectal cancer screening patients

You may qualify if:

• endoscopic diagnosis of colorectal polyp

You may not qualify if:

• colonoscopy result without polyps or IBD diagnosis

Sponsors & Collaborators

• Petz Aladar County Teaching Hospital: lead

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 5, 2020
• First Posted: June 11, 2020
• Study Start: January 5, 2014
• Primary Completion: May 31, 2020
• Study Completion: May 31, 2020
• Last Updated: June 11, 2020

Record last verified: 2020-06