Automatic surveillance ofEscherichia colibacteriological strains within clinical settings

Abstract

Healthcare-associated infections (HAIs) are a significant concern within hospital environments, with the World Health Organization (WHO) identifying them as a major source of bacteriological infections. HAIs affect millions of patients annually, leading to substantial morbidity and mortality. However, a significant proportion of HAIs are preventable through early detection and appropriate intervention and isolation. Traditional methods for identifying bacterial species and strains, such as antigen tests, are often time-consuming and hamper the real-time tracking of outbreaks. The matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) technique has emerged as a more rapid and precise alternative, though it remains limited by manual identification processes and database constraints. Recent advancements have demonstrated the potential of combining MALDI-TOF MS data with machine learning (ML) to enhance in silico identification speed. In this paper we propose the first method for unsupervised Escherichia coli novel strain detection through the application of a efficient PIKE tailored to strain identification, spectral clustering techniques and a one-class support vector machine (OCSVM) novelty detection model, presenting promising results.