Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases.
| Autor: | Berbís MA; Department of R&D, HT Médica, Madrid 28046, Madrid, Spain., Aneiros-Fernández J; Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain., Mendoza Olivares FJ; Department of Gastroenterology, Fatima Clinic, Sevilla 41012, Spain., Nava E; Department of Communications Engineering, University of Málaga, Malaga 29016, Spain., Luna A; MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain. aluna70@htime.org. |
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| Jazyk: | angličtina |
| Zdroj: | World journal of gastroenterology [World J Gastroenterol] 2021 Jul 21; Vol. 27 (27), pp. 4395-4412. |
| DOI: | 10.3748/wjg.v27.i27.4395 |
| Abstrakt: | 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. Competing Interests: Conflict-of-interest statement: Authors declare no conflict of interest for this article. (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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