ArticleClinical endoscopy2022
Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy.
Article in Clinical endoscopy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Artificial intelligence in colonoscopy: Enhancing quality indicators for optimal patient outcomes.World journal of gastroenterology · 2025Review
- Edge Artificial Intelligence Device in Real-Time Endoscopy for the Classification of Colonic Neoplasms.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence-assisted colonoscopy for colorectal lesion detection: a case-control study on diagnostic accuracy and histopathological agreement.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2025Article
- Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials.Clinical endoscopy · 2023Review
- Response to Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.Clinical endoscopy · 2022Article
- Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.Clinical endoscopy · 2022Article
- Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps.Clinical endoscopy · 2022Article
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6 authors.
Funding
Abstract
BACKGROUND/
aimsWe 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.
methodsWe 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.
resultsThe 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).
conclusionOur artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.
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