Letter to the Editor
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Apr 26, 2024; 12(12): 2134-2137
Published online Apr 26, 2024. doi: 10.12998/wjcc.v12.i12.2134
Machine learning in liver surgery: Benefits and pitfalls
Rafael Calleja, Manuel Durán, María Dolores Ayllón, Ruben Ciria, Javier Briceño
Rafael Calleja, Manuel Durán, María Dolores Ayllón, Ruben Ciria, Javier Briceño, Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Córdoba 14004, Spain
Author contributions: Calleja R and Durán M designed and wrote this letter; Ayllón MD, Ciria R and Briceño J performed the group research mentioned in this letter; All authors have read and approve the final manuscript.
Conflict-of-interest statement: The authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or nonfinancial interest in the subject matter or materials discussed in this manuscript. This research has not received any financial support.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rafael Calleja, MD, Research Associate, Surgeon, Hepatobiliary Surgery and Liver Transplantation Unit, Hospital Universitario Reina Sofía, Maimonides Biomedical Research Institute of Cordoba, Avenida Menéndez Pidal s/n, Córdoba 14004, Spain. h12calor@gmail.com
Received: December 20, 2023
Revised: February 8, 2024
Accepted: March 29, 2024
Published online: April 26, 2024
Abstract

The application of machine learning (ML) algorithms in various fields of hepatology is an issue of interest. However, we must be cautious with the results. In this letter, based on a published ML prediction model for acute kidney injury after liver surgery, we discuss some limitations of ML models and how they may be addressed in the future. Although the future faces significant challenges, it also holds a great potential.

Keywords: Machine learning, Liver surgery, Artificial intelligence, Random forest, Prediction model

Core Tip: Artificial intelligence is trending topic in healthcare research. Machine learning classifiers have been explored in the field of liver surgery and liver transplantation. However, despite of promising results, a real applicability is limited by several factors.