CC BY-NC-ND 4.0 · Endosc Int Open 2022; 10(02): E171-E177
DOI: 10.1055/a-1675-1941
Original article

Deep learning and colon capsule endoscopy: automatic detection of blood and colonic mucosal lesions using a convolutional neural network

Miguel Mascarenhas
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
3   Faculty of Medicine of the University of Porto Porto, Portugal
,
Tiago Ribeiro
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
,
João Afonso
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
,
João P.S. Ferreira
4   Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
5   INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
,
Hélder Cardoso
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
3   Faculty of Medicine of the University of Porto Porto, Portugal
,
Patrícia Andrade
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
3   Faculty of Medicine of the University of Porto Porto, Portugal
,
Marco P.L. Parente
4   Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
5   INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
,
Renato N. Jorge
4   Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
5   INEGI – Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal.
,
Miguel Mascarenhas Saraiva
6   ManopH Gastroenterology Clinic, Porto, Portugal
,
Guilherme Macedo
1   Department of Gastroenterology, São João University Hospital, Porto, Portugal
2   WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
3   Faculty of Medicine of the University of Porto Porto, Portugal
› Author Affiliations

Abstract

Background and study aims Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. However, CCE produces long videos, making its analysis time-consuming and prone to errors. Convolutional neural networks (CNN) are artificial intelligence (AI) algorithms with high performance levels in image analysis. We aimed to develop a deep learning model for automatic identification and differentiation of significant colonic mucosal lesions and blood in CCE images.

Patients and methods A retrospective multicenter study including 124 CCE examinations was conducted for development of a CNN model, using a database of CCE images including anonymized images of patients with normal colon mucosa, several mucosal lesions (erosions, ulcers, vascular lesions and protruding lesions) and luminal blood. For CNN development, 9005 images (3,075 normal mucosa, 3,115 blood and 2,815 mucosal lesions) were ultimately extracted. Two image datasets were created and used for CNN training and validation.

Results The mean (standard deviation) sensitivity and specificity of the CNN were 96.3 % (3.9 %) and 98.2 % (1.8 %) Mucosal lesions were detected with a sensitivity of 92.0 % and a specificity of 98.5 %. Blood was detected with a sensitivity and specificity of 97.2 % and 99.9 %, respectively. The algorithm was 99.2 % sensitive and 99.6 % specific in distinguishing blood from mucosal lesions. The CNN processed 65 frames per second.

Conclusions This is the first CNN-based algorithm to accurately detect and distinguish colonic mucosal lesions and luminal blood in CCE images. AI may improve diagnostic and time efficiency of CCE exams, thus facilitating CCE adoption to routine clinical practice.



Publication History

Received: 15 April 2021

Accepted: 21 September 2021

Article published online:
16 February 2022

© 2022. 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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