Polyp Artificial Intelligence Study

Artificial Intelligence Based Colorectal Polyp Histology Prediction by Using Narrow-band Imaging Magnifying Colonoscopy

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.

Study Overview

• Status: Completed
• Conditions: Software Analysis on Polyp Histology Prediction
• Intervention / Treatment: Other: artificial intelligence diagnosis

• Study Type: Observational
• Enrollment (Actual): 373

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 90 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

Inclusion Criteria:

• endoscopic diagnosis of colorectal polyp

Exclusion Criteria:

• colonoscopy result without polyps or IBD diagnosis

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Software accuracy of polyp histology prediction	Artificial intelligence software diagnosis in comparison with the polyp histology	2014-2020

Collaborators and Investigators

• Sponsor: Petz Aladar County Teaching Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 5, 2014
• Primary Completion (Actual): May 31, 2020
• Study Completion (Actual): May 31, 2020

Study Registration Dates

• First Submitted: June 5, 2020
• First Submitted That Met QC Criteria: June 9, 2020
• First Posted (Actual): June 11, 2020

Study Record Updates

• Last Update Posted (Actual): June 11, 2020
• Last Update Submitted That Met QC Criteria: June 9, 2020
• Last Verified: June 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathological Conditions, Anatomical; Polyps
• Other Study ID Numbers: PetzACTHospital

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No