Clinical Endoscopy

  • 제55권1호
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  • Pages.113-121
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  • 2022
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  • 2234-2400(pISSN)
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  • 2234-2443(eISSN)
대한소화기내시경학회 (The Korean Society of Gastrointestinal Endoscopy)
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Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy

  • Istvan Racz (Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital) ;
  • Andras Horvath (Department of Physics and Chemistry, Szechenyi Istvan University) ;
  • Noemi Kranitz (Department of Pathology, Petz Aladar University Teaching Hospital) ;
  • Gyongyi Kiss (Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital) ;
  • Henriett Regoczi (Department of Internal Medicine and Gastroenterology, Petz Aladar University Teaching Hospital) ;
  • Zoltan Horvath (Department of Mathematics and Informatics, Szechenyi Istvan University)
  • 투고 : 2021.05.07
  • 심사 : 2021.06.19
  • 발행 : 2022.01.30
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초록

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.

키워드

과제정보

This study was supported by the GINOP-2.3.4-15-2016-00003 grant.

참고문헌

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