CC BY-NC-ND 4.0 · Endosc Int Open 2021; 09(09): E1361-E1370
DOI: 10.1055/a-1507-4980
Original article

A deep learning framework for autonomous detection and classification of Crohnʼs disease lesions in the small bowel and colon with capsule endoscopy

Tomáš Majtner
1  Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
,
Jacob Broder Brodersen
2  Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
,
Jürgen Herp
1  Applied Artificial Intelligence and Data Science, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
,
Jens Kjeldsen
3  Department of Medical Gastroenterology, Odense University Hospital, Odense, Denmark
,
Morten Lee Halling
2  Department of Internal Medicine, Section of Gastroenterology, Hospital of South West Jutland, Esbjerg, Denmark
,
Michael Dam Jensen
4  Department of Internal Medicine, Section of Gastroenterology, Lillebaelt Hospital, Vejle, Denmark
› Author Affiliations

Abstract

Background and study aims Small bowel ulcerations are efficiently detected with deep learning techniques, whereas the ability to diagnose Crohnʼs disease (CD) in the colon with it is unknown. This study examined the ability of a deep learning framework to detect CD lesions with pan-enteric capsule endoscopy (CE) and classify lesions of different severity.

Patients and methods CEs from patients with suspected or known CD were included in the analysis. Two experienced gastroenterologists classified anonymized images into normal mucosa, non-ulcerated inflammation, aphthous ulceration, ulcer, or fissure/extensive ulceration. An automated framework incorporating multiple ResNet-50 architectures was trained. To improve its robustness and ability to characterize lesions, image processing methods focused on texture enhancement were employed.

Results A total of 7744 images from 38 patients with CD were collected (small bowel 4972, colon 2772) of which 2748 contained at least one ulceration (small bowel 1857, colon 891). With a patient-dependent split of images for training, validation, and testing, ulcerations were diagnosed with a sensitivity, specificity, and diagnostic accuracy of 95.7 % (CI 93.4–97.4), 99.8 % (CI 99.2–100), and 98.4 % (CI 97.6–99.0), respectively. The diagnostic accuracy was 98.5 % (CI 97.5–99.2) for the small bowel and 98.1 % (CI 96.3–99.2) for the colon. Ulcerations of different severities were classified with substantial agreement (κ = 0.72).

Conclusions Our proposed framework is in excellent agreement with the clinical standard, and diagnostic accuracies are equally high for the small bowel and colon. Deep learning approaches have a great potential to help clinicians detect, localize, and determine the severity of CD with pan-enteric CE.



Publication History

Received: 20 January 2021

Accepted: 03 May 2021

Publication Date:
16 August 2021 (online)

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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