Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market.
Dig Liver Dis
; 56(7): 1156-1163, 2024 Jul.
Article
en En
| MEDLINE
| ID: mdl-38763796
ABSTRACT
Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.
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Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Neoplasias Gástricas
/
Inteligencia Artificial
/
Aprendizaje Profundo
Idioma:
En
Revista:
Dig Liver Dis
Asunto de la revista:
GASTROENTEROLOGIA
Año:
2024
Tipo del documento:
Article