Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27(27): 4395-4412 [PMID: 34366612 DOI: 10.3748/wjg.v27.i27.4395]
Corresponding Author of This Article
Antonio Luna, MD, PhD, Doctor, MRI Unit, Department of Radiology, HT Médica, C/ Carmelo Torres 2, Jaén 23007, Spain. aluna70@htime.org
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Jul 21, 2021; 27(27): 4395-4412 Published online Jul 21, 2021. doi: 10.3748/wjg.v27.i27.4395
Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases
M Alvaro Berbís, José Aneiros-Fernández, F Javier Mendoza Olivares, Enrique Nava, Antonio Luna
M Alvaro Berbís, Department of R&D, HT Médica, Madrid 28046, Madrid, Spain
José Aneiros-Fernández, Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain
F Javier Mendoza Olivares, Department of Gastroenterology, Fatima Clinic, Sevilla 41012, Spain
Enrique Nava, Department of Communications Engineering, University of Málaga, Malaga 29016, Spain
Antonio Luna, MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain
Author contributions: All authors contributed to this paper with literature review and analysis and approval of the final version.
Conflict-of-interest statement: Authors declare no conflict of interest for this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Antonio Luna, MD, PhD, Doctor, MRI Unit, Department of Radiology, HT Médica, C/ Carmelo Torres 2, Jaén 23007, Spain. aluna70@htime.org
Received: January 28, 2021 Peer-review started: January 28, 2021 First decision: March 29, 2021 Revised: April 14, 2021 Accepted: June 7, 2021 Article in press: June 7, 2021 Published online: July 21, 2021 Processing time: 171 Days and 15.6 Hours
Abstract
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.
Core Tip: Artificial intelligence in general, and machine learning (ML) in particular, have great potential as supporting tools for physicians in the evaluation of neoplastic diseases and other conditions of the gastrointestinal tract. Radiology, endoscopy and pathology images can be read and interpreted using ML approaches in a wide variety of clinical scenarios. These include detection, classification and automatic segmentation of tumor lesions, tumor grading, patient stratification and prediction of treatment response.
