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

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2024

Autores

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

Revista

Título: Diagnostics

Vol. 14

Página Final: 291

Editora: MDPI

Indexação

ISI Web of Knowledge - 0 Citações

Scopus

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 21