Accelerated detection of <i>Clostridioides difficile</i> sequence type 37 by integrating MALDI-TOF mass spectrometry with artificial neural network

<i>C. difficile</i> ST37 (RT017) is a highly virulent strain that often causes severe infections and is frequently resistant to antibiotics such as fluoroquinolones and clindamycin, which are known to promote <i>C. difficile</i> infection. Rapid identification of this strain is essential to ensure timely clinical intervention and effective infection control. Current detection methods rely on lengthy and labor-intensive procedures, delaying treatment decisions. This study introduces a new, rapid identification method combining mass spectrometry with machine learning. The developed artificial neural network can accurately distinguish the ST37 strain in approximately 10 seconds, significantly reducing diagnostic time compared to traditional methods. Implementing this fast, reliable, and economical diagnostic tool in clinical laboratories will enhance patient care by facilitating quicker diagnosis and targeted therapy, thus minimizing the risk of severe complications associated with <i>C. difficile</i> infections.