Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study

Development and validation of a hierarchical machine learning method using MALDI-TOF mass spectrometry for rapid SCCmec typing and PVL detection in MRSA: a multi-centre study

Objectives Methicillin-resistant Staphylococcus aureus (MRSA) is a major public health concern because of its genotypic diversity and association with severe infections, particularly those caused by strains carrying Panton–Valentine leukocidin (PVL). This study aimed to develop an artificial intelli...

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Main Authors: Tai-Han Lin, Ming-Jr Jian, Fujiko Mitsumoto-Kaseida, Norihito Kaku, Hsing-Yi Chung, Chih-Kai Chang, Cherng-Lih Perng, Yung-Chih Wang, Chih-Chien Wang, Yuan-Hao Chen, Katsunori Yanagihara, Hung-Sheng Shang
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Emerging Microbes and Infections
Subjects:
MRSA
PVL
artificial intelligence
artificial intelligence-clinical decision support systems
AI-CDSS
Online Access:https://www.tandfonline.com/doi/10.1080/22221751.2025.2525264
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https://www.tandfonline.com/doi/10.1080/22221751.2025.2525264

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