Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Aug 28, 2021; 2(4): 127-135
Published online Aug 28, 2021. doi: 10.37126/aige.v2.i4.127
Deep learning applied to the imaging diagnosis of hepatocellular carcinoma
Vinícius Remus Ballotin, Lucas Goldmann Bigarella, John Soldera, Jonathan Soldera
Vinícius Remus Ballotin, Lucas Goldmann Bigarella, School of Medicine, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
John Soldera, Computer Science, Federal Institute of Education, Science and Technology Farroupilha, Santo Ângelo 98806-700, RS, Brazil
Jonathan Soldera, Clinical Gastroenterology, Universidade de Caxias do Sul, Caxias do Sul 95070-560, RS, Brazil
Author contributions: All authors contributed to study concept and design, to drafting of the manuscript and to critical revision of the manuscript for important intellectual content.
Conflict-of-interest statement: The authors have no conflict of interest to disclose.
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:
Corresponding author: Jonathan Soldera, MD, MSc, Associate Professor, Staff Physician, Clinical Gastroenterology, Universidade de Caxias do Sul, Rua Francisco Getúlio Vargas 1130, Caxias do Sul 95070-560, RS, Brazil.
Received: April 21, 2021
Peer-review started: April 21, 2021
First decision: May 19, 2021
Revised: June 5, 2021
Accepted: July 19, 2021
Article in press: July 19, 2021
Published online: August 28, 2021
Core Tip

Core Tip: Hepatocellular carcinoma is diagnosed using imaging techniques, such as computed tomography and magnetic resonance imaging. In order to improve outcomes and bypass obstacles, many companies and clinical centers have been trying to develop deep learning systems that could be able to diagnose and classify liver nodules in the cirrhotic liver. Neural networks have become one of the most efficient approaches to accurately diagnose liver nodules using deep learning systems. Therefore, with the improvement of these techniques in the long term, they could be applicable in daily practice, modifying outcomes.