Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy
Clinical Endoscopy 2022³â 55±Ç 1È£ p.113 ~ p.121
(Racz Istvan) - Petz Aladar University Teaching Hospital Department of Internal Medicine and Gastroenterology
(Horvath Andras) - Szechenyi Istvan University Department of Physics and Chemistry
(Kranitz Noemi) - Petz Aladar University Teaching Hospital Department of Pathology
(Kiss Gyongyi) - Petz Aladar University Teaching Hospital Department of Internal Medicine and Gastroenterology
(Regoczi Henriett) - Petz Aladar University Teaching Hospital Department of Internal Medicine and Gastroenterology
(Horvath Zoltan) - Szechenyi Istvan University Department of Mathematics and Informatics
Abstract
Background/Aims: We have been developing artificial intelligence based polyp histology prediction (AIPHP) method to classify Narrow Band Imaging (NBI) magnifying colonoscopy images to predict the hyperplastic or neoplastic histology of polyps. Our aim was to analyze the accuracy of AIPHP and narrow-band imaging international colorectal endoscopic (NICE) classification based histology predictions and also to compare the results of the two methods.
Methods: We studied 373 colorectal polyp samples taken by polypectomy from 279 patients. The documented NBI still images were analyzed by the AIPHP method and by the NICE classification parallel. The AIPHP software was created by machine learning method. The software measures five geometrical and color features on the endoscopic image.
Results: The accuracy of AIPHP was 86.6% (323/373) in total of polyps. We compared the AIPHP accuracy results for diminutive and non-diminutive polyps (82.1% vs. 92.2%; p=0.0032). The accuracy of the hyperplastic histology prediction was significantly better by NICE compared to AIPHP method both in the diminutive polyps (n=207) (95.2% vs. 82.1%) (p<0.001) and also in all evaluated polyps (n=373) (97.1% vs. 86.6%) (p<0.001)
Conclusions: Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.
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Artificial intelligence, Colorectal polyps, Histology prediction, Narrow band imaging, Narrow-band imaging international colorectal endoscopic classification
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The authors developed artificial intelligence-based polyp histology prediction (AIPHP) software to automatically evaluate the magnified NBI colonoscopy images aiming the histology prediction of polyps. This article report the development of our software that can evaluate colorectal polyps using selectively recorded still colonoscopic images.