RT Journal Article
T1 Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.
A1 Berbís, M Alvaro
A1 Aneiros-Fernández, José
A1 Mendoza Olivares, F Javier
A1 Nava, Enrique
A1 Luna, Antonio
K1 Artificial intelligence
K1 Endoscopy
K1 Gastroenterology
K1 Machine learning
K1 Pathology
K1 Radiology
AB The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.
YR 2021
FD 2021
LK http://hdl.handle.net/10668/18335
UL http://hdl.handle.net/10668/18335
LA en
DS RISalud
RD Apr 11, 2025