Software Analysis on Polyp Histology Prediction
Conditions
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.
Interventions
artificial intelligence prediction of colorectal polyp histology
Sponsors
Petz Aladar County Teaching Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
No
Inclusion criteria
* endoscopic diagnosis of colorectal polyp
Exclusion criteria
* colonoscopy result without polyps or IBD diagnosis
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Software accuracy of polyp histology prediction | 2014-2020 | Artificial intelligence software diagnosis in comparison with the polyp histology |
Outcome results
None listed