Polyp Artificial Intelligence Study

Software Analysis on Polyp Histology Prediction Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04425941
• Other study ID #: PetzACTHospital
• Status: Completed
• Start date: January 5, 2014
• Est. completion date: May 31, 2020

Study information

• Verified date: June 2020
• Source: Petz Aladar County Teaching Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 373
• Est. completion date: May 31, 2020
• Est. primary completion date: May 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 90 Years

Eligibility

Inclusion Criteria:

• endoscopic diagnosis of colorectal polyp

Exclusion Criteria:

• colonoscopy result without polyps or IBD diagnosis

Related Conditions & MeSH terms

• Polyps
• Software Analysis on Polyp Histology Prediction

Intervention

Other:
• artificial intelligence diagnosis
artificial intelligence prediction of colorectal polyp histology

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Petz Aladar County Teaching Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Software accuracy of polyp histology prediction	Artificial intelligence software diagnosis in comparison with the polyp histology	2014-2020