Software Analysis on Polyp Histology Prediction Clinical Trial
Official title:
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.
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