NCT04425941

Polyp Artificial Intelligence Study

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 373 (actual)
• Sponsor: Petz Aladar County Teaching Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 90 Years
• Healthy volunteers: Not accepted

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

Type	Name	Description
OTHER	artificial intelligence diagnosis	artificial intelligence prediction of colorectal polyp histology

Timeline

• Start date: 2014-01-05
• Primary completion: 2020-05-31
• Completion: 2020-05-31
• First posted: 2020-06-11
• Last updated: 2020-06-11