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Polyp Artificial Intelligence Study

NCT04425941 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 373

Last updated 2020-06-11

No results posted yet for this study

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

Interventions

OTHER

artificial intelligence diagnosis

artificial intelligence prediction of colorectal polyp histology

Sponsors & Collaborators

  • Petz Aladar County Teaching Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Max Age
90 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2014-01-05
Primary Completion
2020-05-31
Completion
2020-05-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04425941 on ClinicalTrials.gov