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

• Type: Another Publication in an International Scientific Journal
• Date: 2024

Authors

Mota, J (Author) Other; Almeida, MJ (Author) Other; Mendes, F (Author) Other; Martins, M (Author) Other; Ribeiro, T (Author) Other; Afonso, J (Author) Other; Cardoso, P (Author) Other; Cardoso, H (Author) Other; Andrade, P (Author) Other; Ferreira, J (Author) FEUP; Mascarenhas, M (Author) Other; Macedo G (Author) FMUP

Journal

Title: Diagnostics

Vol. 14

Final page: 291

Publisher: MDPI

Indexing

ISI Web of Knowledge - 0 Citations

Scopus

Other information

Authenticus ID: P-010-03Q

DOI: 10.3390/diagnostics14030291

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21