Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy
- Resource Type
- Authors
- Zoltán Horváth; Gyöngyi Kiss; András Horváth; Istvan Racz; Henriett Regoczi; Noemi Kranitz
- Source
- Clinical Endoscopy, Vol 55, Iss 1, Pp 113-121 (2022)
- Subject
- histology prediction
medicine.medical_treatment
narrow band imaging
Medicine (miscellaneous)
RC799-869
Magnifying colonoscopy
medicine
Radiology, Nuclear Medicine and imaging
narrow-band imaging international colorectal endoscopic classification
Internal medicine
Narrow-band imaging
business.industry
Gastroenterology
Histology
Diseases of the digestive system. Gastroenterology
artificial intelligence
colorectal polyps
RC31-1245
digestive system diseases
Polypectomy
Narrow band
Colorectal Polyp
Artificial intelligence
business
Large size
Endoscopic image
- Language
- ISSN
- 2234-2400
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