Innovative strategies against superbugs: Developing an AI-CDSS for precise Stenotrophomonas maltophilia treatment.

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Abstract

The World Health Organization named Stenotrophomonas maltophilia (SM) a critical multi-drug resistant threat, necessitating rapid diagnostic strategies. Traditional culturing methods require up to 96 h, including 72 h for bacterial growth, identification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) through protein profile analysis, and 24 h for antibiotic susceptibility testing. In this study, we aimed at developing an artificial intelligence-clinical decision support system (AI-CDSS) by integrating MALDI-TOF MS and machine learning to quickly identify levofloxacin and trimethoprim/sulfamethoxazole resistance in SM, optimizing treatment decisions.

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Author and article information

Journal

Journal ID (iso-abbrev): J Glob Antimicrob Resist

Title: Journal of global antimicrobial resistance

Publisher: Elsevier BV

ISSN (Electronic): 2213-7173

ISSN (Print): 2213-7165

Publication date (Electronic): Sep 2024

Volume: 38

Affiliations

[1 ] Department of Pathology, Division of Clinical Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China.

[2 ] Department of Pathology, Division of Clinical Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China; Graduate Institute of Medical Science, National Defense Medical Center, Taipei, Taiwan, Republic of China.

[3 ] Department of Internal Medicine, Division of Infectious Diseases and Tropical Medicine, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan, Republic of China.

[4 ] Department of Internal Medicine, Division of Pulmonary and Critical Care Medicine, Tri-Service General Hospital, National Defense Medical Centre, Taipei, Taiwan, Republic of China.

[5 ] Department of Pathology, Division of Clinical Pathology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan, Republic of China. Electronic address: iamkeith001@gmail.com.

Article

Publisher Item ID: S2213-7165(24)00113-9

DOI: 10.1016/j.jgar.2024.06.004

PubMed ID: 38909685

SO-VID: 4d3d9bc8-1589-4450-b66d-58c4df6f7436

History

Keywords: MALDI-TOF MS,Trimethoprim/sulfamethoxazole resistance,Stenotrophomonas maltophilia,Machine learning,AI-CDSS

Data availability:

Keywords: MALDI-TOF MS, Trimethoprim/sulfamethoxazole resistance, Stenotrophomonas maltophilia, Machine learning, AI-CDSS

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