Artificial Intelligence-Based Colorectal Polyp Histology Prediction by Using Narrow-Band Image-Magnifying Colonoscopy

Overview

Journal Clin Endosc

Date 2021 Sep 23

PMID 34551512

Citations 4

Authors

Istvan Racz

Andras Horvath

Noemi Kranitz

Gyongyi Kiss

Henriett Regoczi

Zoltan Horvath

Affiliations

Soon will be listed here.

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).

Conclusion: Our artificial intelligence based polyp histology prediction software could predict histology with high accuracy only in the large size polyp subgroup.

Citing Articles

Detecting colorectal lesions with image-enhanced endoscopy: an updated review from clinical trials.

Nagai M, Suzuki S, Minato Y, Ishibashi F, Mochida K, Ohata K Clin Endosc. 2023; 56(5):553-562.

PMID: 37491990 PMC: 10565430. DOI: 10.5946/ce.2023.055.

Response to Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.

Racz I, Horvath A, Horvath Z Clin Endosc. 2022; 55(5):701-702.

PMID: 36205048 PMC: 9539298. DOI: 10.5946/ce.2022.123.1.

Artificial intelligence-based colorectal polyp histology prediction using narrow-band image-magnifying colonoscopy: a stepping stone for clinical practice.

Chang J Clin Endosc. 2022; 55(5):699-700.

PMID: 36205047 PMC: 9539285. DOI: 10.5946/ce.2022.123.

Artificial Intelligence-Based Colorectal Polyp Histology Prediction: High Accuracy in Larger Polyps.

Muguruma N, Takayama T Clin Endosc. 2022; 55(1):45-46.

PMID: 34974677 PMC: 8831397. DOI: 10.5946/ce.2021.266.

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