Overcoming Challenges of Reproducibility and Variability for the Clostridioides difficile typification

The implementation of Matrix-assisted laser desorption ionization–time of flight (MALDI-TOF) mass spectrometry has had a profound impact on clinical microbiology, facilitating rapid bacterial identification through protein profile analysis. However, the application of this technique is limited by challenges related to the reproducibility and variability of spectra, particularly in distinguishing closely related bacterial strains, as exemplified by the typification of Clostridioides difficile ribotypes. This thesis investigates the integration of Machine Learning algorithms to enhance the robustness and accuracy of MALDI-TOF spectra analysis. The aim is to compare traditional classifiers in order to gain insight into how spectral variability affects their performance in typification. Furthermore, specific data augmentation techniques for MALDI-TOF spectra are designed to enhance the classification of C. difficile ribotypes, to alleviate the inherent variability in MALDI-TOF spectra, and to address the issue of limited sample sizes. The results demonstrate that these methods can significantly enhance the accuracy of classification of C. difficile strains, thereby rendering MALDI-TOF a more reliable tool in clinical diagnostics.