Staphylococcus epidermidis is a pervasive coloniser of healthy human skin, but is also a notorious source of serious nosocomial infections found in hospital devices.
Scientists have discovered a way in which artificial intelligence can be used to predict whether an individual is carrying the potential killer staphylococcus epidermidis on their skin. Moreover, this particular skin inhabitant has been known to be the source of nosocomial infections, which is essentially infections that have been caught in a hospital and are potentially caused by organisms that are resistant to antibiotics.
Using AI to predict occurrence of Nosocomial infections
Scientists Johan Pensar and Jukka Corander from Aalto-University and the University of Helsinki, Finland, joined a team of microbiologists and geneticists to delve further into the world of infections and diseases.
By combining large-scale population genomics and in vitro measurements of immunologically relevant features of staphylococcus epidermidis and nosocomial infections, 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 staphylococcus epidermidis.
What do you know about staphylococcus epidermidis?
The bacterial inhabitant staphylococcus epidermidis mainly colonises human skin and is a major health concern due to its involvement in hospital-acquired infections, otherwise known as nosocomial infections. Such infections are usually found from devices and surgical procedures such as hip replacements.
It has not been known whether all members of the staphylococcus epidermidis population colonising the skin asymptomatically can cause such infections, or if some of them have an increased tendency to do so when they enter either the bloodstream or a deep tissue.