Editorial
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Jun 8, 2025; 6(1): 101264
Published online Jun 8, 2025. doi: 10.35711/aimi.v6.i1.101264
Comprehensive study comparing different machine learning methods in computed tomography imaging
Mustafa Erdem Sağsöz
Mustafa Erdem Sağsöz, Department of Biophysics, Ataturk University, Erzurum 25050, Türkiye
Author contributions: Sağsöz ME was responsible for the concept, design, and writing of the paper.
Conflict-of-interest statement: The author reports no relevant conflicts 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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Mustafa Erdem Sağsöz, PhD, Associate Professor, Department of Biophysics, Ataturk University, Erzurum 25050, Türkiye. mesagsoz@atauni.edu.tr
Received: September 9, 2024
Revised: April 3, 2025
Accepted: April 15, 2025
Published online: June 8, 2025
Processing time: 269 Days and 21.5 Hours
Core Tip

Core Tip: This review is about the article written by Zhao et al. This study compares different machine learning methods in computed tomography imaging. In this study support vector machines, a vector space-based machine learning algorithm that finds a decision boundary between the two classes that are farthest from any point in the training data, was found to be the most effective model in the arterial and venous phases.