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 differentiation of Clostridioides difficile ribotypes. This study evaluates the integration of Machine Learning algorithms to enhance the robustness and accuracy of MALDI-TOF spectra analysis. For this purpose, we compared traditional classifiers to gain insight into how spectral variability affects their performance in bacterial typing. Furthermore, specific Data Augmentation techniques for MALDI-TOF spectra were designed to reduce 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 especially when facing variability in the sample acquisition process. This highlights that the combination of domain adaptation with machine learning (ML) could make MALDI-TOF a more versatile and reliable tool in clinical diagnostics.