Comprehensive study comparing different machine learning methods in computed tomography imaging

doi:10.35711/aimi.v6.i1.101264

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Jun 8, 2025 (publication date) through Mar 4, 2026

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Artificial Intelligence in Medical Imaging

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2644-3260

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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.

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.