Polyp Artificial Intelligence Study

Completed

• Conditions: Software Analysis on Polyp Histology Prediction

• Registration Number: NCT04425941
• Lead Sponsor: Petz Aladar County Teaching Hospital

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2014-01-05
• Primary Completion: 2020-05-31
• Study Completion: 2020-05-31
• Status Verified: 2020-06
• Study First Submitted: 2020-06-05
• Study First Submitted QC: 2020-06-09
• Last Update Submitted: 2020-06-09
• Study First Posted: 2020-06-11
• Last Update Posted: 2020-06-11

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 373

Inclusion Criteria

• endoscopic diagnosis of colorectal polyp

Exclusion Criteria

• colonoscopy result without polyps or IBD diagnosis

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Software accuracy of polyp histology prediction	2014-2020	Artificial intelligence software diagnosis in comparison with the polyp histology