Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning
The worldwide increase of antimicrobial resistance (AMR) is a serious threat to human health. To avert the spread of AMR, fast reliable diagnostics tools that facilitate optimal antibiotic stewardship are an unmet need. In this regard, Raman spectroscopy promises rapid label- and culture-free identification and antimicrobial susceptibility testing (AST) in a single step. However, even though many Raman-based bacteria-identification and AST studies have demonstrated impressive results, some shortcomings must be addressed. To bridge the gap between proof-of-concept studies and clinical application, we have developed machine learning techniques in combination with a novel data-augmentation algorithm, for fast identification of minimally prepared bacteria phenotypes and the distinctions of methicillin-resistant (MR) from methicillin-susceptible (MS) bacteria.
Global Ambassador Network
Welcome at the AMR Insights Ambassador Network!
The AMR Insights Ambassador Network is a growing, distinctive group of professionals who stand out for their commitment, willingness to cooperate and open attitude to combat Antimicrobial resistance (AMR).