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
AI-CDSS is a scalable and efficient method for MRSA genotyping and PVL detection. By streamlining diagnostics and supporting timely clinical interventions, this system can improve infection management and patient care, which will reduce mortality associated with MRSA infections.
Authors
- Katsunori Yanagihara (ORCID: https://orcid.org/0000-0003-3677-2486)
- Fujiko Mitsumoto‐Kaseida (ORCID: https://orcid.org/0000-0002-4825-5657)
- Hung‐Sheng Shang (ORCID: https://orcid.org/0000-0002-4831-1866)
- Cherng‐Lih Perng (ORCID: https://orcid.org/0000-0001-7182-1031)
- Ming‐Jr Jian (ORCID: https://orcid.org/0000-0002-4902-4447)
- Hsing‐Yi Chung (ORCID: https://orcid.org/0000-0002-7870-8354)
- Yuan-Hao Chen (ORCID: https://orcid.org/0000-0002-0570-4328)
- Yung-Chih Wang (ORCID: https://orcid.org/0000-0002-8491-8972)
- Tai‐Han Lin (ORCID: https://orcid.org/0000-0002-6100-3120)
- Norihito Kaku (ORCID: https://orcid.org/0000-0002-4086-4853)
- Chih-Kai Chang
- Chih‐Chien Wang (ORCID: https://orcid.org/0009-0009-7546-644X)
Institutions
- Tri-Service General Hospital (TW)
- Nagasaki University (JP)
- National Defense Medical College (JP)
- National Defense Medical Center (TW)
Publication Details
- Journal
- Emerging Microbes & Infections
- Published
- 2025-06-27
- DOI
- https://doi.org/10.1080/22221751.2025.2525264
- Citations
- 1
- Primary Topic
- Bacterial Identification and Susceptibility Testing
- Type
- article
- Field-Weighted Citation Impact
- 1.32
Funders
- Tri-Service General Hospital