Accurate and fast identification of minimally prepared bacteria phenotypes using Raman spectroscopy assisted by machine learning

  02 October 2022

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. 

Further reading: Nature
Author(s): Benjamin Lundquist Thomsen et al
Smart Innovations  
Back

OUR UNDERWRITERS

Unrestricted financial support by:

Antimicrobial Resistance Fighter Coalition

Evotec

JSS University





Technology Database

Display your AMR Technology, Product and Service

Suppliers and Users of Technologies, Products and Services benefit from CAPI.
CAPI (Continuous AMR Partnering Initiative) unites Suppliers and Users worldwide with the aim to add to the curbing of AMR.

Read more and make your own Technology Page >>
What is going on with AMR?
Stay tuned with remarkable global AMR news and developments!