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Review
. 2024 Jan 29;14(3):291.
doi: 10.3390/diagnostics14030291.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

Joana Mota  1   2 , Maria João Almeida  1   2 , Francisco Mendes  1   2 , Miguel Martins  1   2 , Tiago Ribeiro  1   2 , João Afonso  1   2 , Pedro Cardoso  1   2 , Helder Cardoso  1   2   3 , Patrícia Andrade  1   2   3 , João Ferreira  4   5 , Miguel Mascarenhas  1   2   3   6 , Guilherme Macedo  1   2   3
Affiliations
Review

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

Joana Mota et al. Diagnostics (Basel). .

Abstract

The role of capsule endoscopy and enteroscopy in managing various small-bowel pathologies is well-established. However, their broader application has been hampered mainly by their lengthy reading times. As a result, there is a growing interest in employing artificial intelligence (AI) in these diagnostic and therapeutic procedures, driven by the prospect of overcoming some major limitations and enhancing healthcare efficiency, while maintaining high accuracy levels. In the past two decades, the applicability of AI to gastroenterology has been increasing, mainly because of the strong imaging component. Nowadays, there are a multitude of studies using AI, specifically using convolutional neural networks, that prove the potential applications of AI to these endoscopic techniques, achieving remarkable results. These findings suggest that there is ample opportunity for AI to expand its presence in the management of gastroenterology diseases and, in the future, catalyze a game-changing transformation in clinical activities. This review provides an overview of the current state-of-the-art of AI in the scope of small-bowel study, with a particular focus on capsule endoscopy and enteroscopy.

Keywords: artificial intelligence; capsule endoscopy; convolutional neural network; deep learning; device-assisted enteroscopy; small bowel.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Capsule endoscopy images of several small-bowel pathologies. In the top row, the left image corresponds to normal mucosa, and the two images on the right illustrate vascular lesions. In the bottom row, the left image corresponds to a protruding lesion, the center image to an ulcer, and the right image to hematic residues.
Figure 2
Figure 2
Images depicting the quality of small-bowel preparation. The left image corresponds to a satisfactory preparation. The right image corresponds to an excellent preparation.
Figure 3
Figure 3
Heatmaps obtained from the application of the convolutional neural network showing pleomorphic lesions identified during small-bowel capsule endoscopy.
Figure 4
Figure 4
Heatmaps obtained from the application of the convolutional neural network showing pleomorphic lesions identified during device-assisted enteroscopy.
See this image and copyright information in PMC

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