-
Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy
-
Istvan Racz, Andras Horvath, Noemi Kranitz, Gyongyi Kiss, Henriett Regoczi, Zoltan Horvath
-
Clin Endosc 2022;55(1):113-121. Published online September 23, 2021
-
DOI: https://doi.org/10.5946/ce.2021.149
-
-
 Abstract
 PDF PubReader ePub
- 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.
-
Citations
Citations to this article as recorded by  - Colon polyps: updates in classification and management
David Dornblaser, Sigird Young, Aasma Shaukat
Current Opinion in Gastroenterology.2024; 40(1): 14. CrossRef - Employing deep learning for predicting the thermal properties of water and nano-encapsulated phase change material
Saihua Xu, Ali Basem, Hasan A Al-Asadi, Rishabh Chaturvedi, Gulrux Daminova, Yasser Fouad, Dheyaa J Jasim, Javid Alhoee
International Journal of Low-Carbon Technologies.2024; 19: 1453. CrossRef - Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms
Ryosuke Kikuchi, Kazuaki Okamoto, Tsuyoshi Ozawa, Junichi Shibata, Soichiro Ishihara, Tomohiro Tada
Digestion.2024; : 1. CrossRef - Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials
Mizuki Nagai, Sho Suzuki, Yohei Minato, Fumiaki Ishibashi, Kentaro Mochida, Ken Ohata, Tetsuo Morishita
Clinical Endoscopy.2023; 56(5): 553. CrossRef - AI-powered medical devices for practical clinicians including the diagnosis of colorectal polyps
Donghwan Kim, Eunsun Kim
Journal of the Korean Medical Association.2023; 66(11): 658. CrossRef - Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps
Naoki Muguruma, Tetsuji Takayama
Clinical Endoscopy.2022; 55(1): 45. CrossRef - Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice
Ji Young Chang
Clinical Endoscopy.2022; 55(5): 699. CrossRef - Response to Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice
Istvan Racz, Andras Horvath, Zoltán Horvath
Clinical Endoscopy.2022; 55(5): 701. CrossRef
-
5,022
View
-
205
Download
-
7
Web of Science
-
8
Crossref
|
KSGE
