Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	101264
Country	Türkiye

Category	Radiology, Nuclear Medicine & Medical Imaging
Manuscript Type	Editorial
Article Title	Comprehensive study comparing different machine learning methods in computed tomography imaging
Manuscript Source	Invited Manuscript
All Author List	Mustafa Erdem Sağsöz
Funding Agency and Grant Number
Corresponding Author	Mustafa Erdem Sağsöz, Associate Professor, PhD, Department of Biophysics, Ataturk University, Lojmanlari Blok:11/1 25240, Erzurum 25050, Türkiye. mesagsoz@atauni.edu.tr
Key Words	Deep learning; Perineural invasion; Radiomics; Rectal cancer; Stacking nomogram; Support vector machines
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.
Citation	Sağsöz ME. Comprehensive study comparing different machine learning methods in computed tomography imaging. Artif Intell Med Imaging 2025; In press

Received	2024-09-09 08:58
Peer-Review Started	2024-09-09 08:58
To Make the First Decision
Return for Revision	2025-03-11 08:48
Revised	2025-04-03 11:27
Second Decision	2025-04-15 02:42
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-04-15 09:51
Articles in Press	2025-04-15 09:51
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2025-04-30 05:52

ISSN	2644-3260 (online)
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