Portal de investigação
Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation
Data de publicação:
Data Ahead of Print:
Autores da FMUP
Participantes de fora da FMUP
- Kerdegari, Hamideh
- Higgins, Kyle
- Veselkov, Dennis
- Laponogov, Ivan
- Polaka, Inese
- Coimbra, Miguel
- Pescino, Junior Andrea
- Leja, Marcis
- Fleitas Kanonnikoff, Tania
- Veselkov, Kirill
Unidades de investigação
Abstract
The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, which is a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia, and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FMs), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FMs in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FMs into clinical practice for the prevention/management of GC cases, thereby improving patient outcomes.
Dados da publicação
- ISSN/ISSNe:
- 2075-4418, 2075-4418
- Tipo:
- Review
- Páginas:
- -
- Link para outro recurso:
- www.scopus.com
Diagnostics MDPI AG
Documentos
- Não há documentos
Filiações
Keywords
- gastric cancer; endoscopy; pathology; foundation models
Proyectos asociados
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Investigador Principal: Mário Jorge Dinis Ribeiro
Estudo Clínico Académico (IMAGE) . AgênciaD&C . 2021
Effectiveness of endoscopic resection of colonic lesions > 20mm
Investigador Principal: Mário Jorge Dinis Ribeiro
Estudo Clínico Académico (Colonic lesions) . 2020
Prevalence and Prognostic Importance of Occult Tumor Cells in Gastric Cancer
Investigador Principal: Mário Jorge Dinis Ribeiro
Estudo Clínico Académico . 2022
Citar a publicação
Kerdegari H,Higgins K,Veselkov D,Laponogov I,Polaka I,Coimbra M,Pescino JA,Leja M,Dinis M,Fleitas T,Veselkov K. Foundational Models for Pathology and Endoscopy Images: Application for Gastric Inflammation. Diagn. 2024. 14(17):1912. IF:3,600. (2).
