It has not been known whether all members of the S. epidermidis
population colonizing the skin asymptomatically are capable of causing such infections, or if some of them have a heightened tendency to do so when they enter either the bloodstream or a deep tissue.
‘Artificial Intelligence devices help identify high-risk bacterial infections proactively before a surgical procedure.’
FCAI scientists Johan Pensar and Jukka Corander from the Aalto-University and the University of Helsinki, joined a team of microbiologists and geneticists to unravel this mystery. By combining large-scale population genomics and in vitro measurements of immunologically relevant features of these bacteria, they were able to use machine learning to successfully predict the risk of developing a serious, and possibly life-threatening infection from the genomic features of a bacterial isolate.
This opens the door for future technology where high-risk genotypes are identified proactively when a person is to undergo a surgical procedure, which has high potential to reduce the burden of nosocomial infections caused by S. epidermidis