Examinando por Materia "Resistencia bacteriana"

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    Detection of carbapenem resistance in Klebsiella pneumoniae using convolutional vision transformers and MALDI-TOF proteomic profiles.
    (Universidad EAFIT, 2025) Salazar Marín, Valentina; Fernández García, Geysson Javier; Bravo Ortíz, Mario Alejandro
    Antimicrobial resistance is a growing global health problem, significantly increasing morbidity, mortality and healthcare costs. Traditionally, the identification of antibiotic resistance is based on phenotypic methods such as agar diffusion or automated systems such as VITEK, which require 24-72 hours to yield definitive results, delaying appropriate patient management. In this context, the need for faster and more accurate diagnostic aid strategies arises. This study explores the integration of artificial intelligence (AI) and mass spectrometry techniques for the classification of carbapenem-resistant Klebsiella pneumoniae strains. Specifically, matrix-assisted laser desorption/ionization-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) data were used to generate proteomic profiles potentially associated with resistance mechanisms. However, the complexity and high volume of these data make the use of AI tools capable of identifying robust patterns indispensable. A convolutional vision transformer (CVT) model was implemented to classify carbapenem-resistant Klebsiella pneumoniae strains from a set of 180 proteomic spectra collected by Synlab Colombia. The CVT model outperformed traditional convolutional neural networks and other automated learning approaches, achieving higher accuracy and stability. Grad-CAM visualization improved model interpretability by identifying key spectral regions associated with resistance. The results highlight the potential of Vision Transformers in microbiological diagnostics by significantly reducing resistance detection time and contributing to a timelier clinical response. Future studies should explore the applicability of this methodology on other resistant pathogens to improve global surveillance efforts against antimicrobial resistance.