× Business
TelecomHealthcareDigital MarketingERPRetailMedia and EntertainmentOil and GasFood and BeveragesMarketing and AdvertisingBanking and InsuranceMetals and MiningLegalComplianceCryptocurrency
Big DataCloudIT ServiceSoftwareMobileSecurityNetworkingStorageCyber SecuritySAPData AnalysisloTBio TechQuality AssuranceEducationE-commerceGaming and VFXArtificial Intelligencescience-and-technology
Cisco DATABASE Google IBM Juniper Microsoft M2M Oracle Red hat Saas SYMANTEC
CEO ReviewCMO ReviewCFO ReviewCompany Review
Startups Opinion Yearbook Readers Speak Contact Us

Scientists Develop AI-based Tool which can predict the Risk of Infection after Surgery

siliconreview Scientists Develop AI-based Tool which can predict the Risk of Infection after Surgery

A team of scientists from Finland has created an AI-based tool which has the potential to successfully predict surgery infections and warn the patient whether he/she is at a risk of developing serious infections after surgeries.

The scientists are from Aalto University and the University of Helsinki.

Most of the infection is caused by a bacterium called Staphylococcus epidermidis (a ubiquitous colonizer of healthy human skin).  The bacteria may cause infection when any indwelling devices are inserted during surgeries like hip replacement surgery.

But, it’s not clear whether all members of the bacteria colonizing the skin asymptomatically are capable of causing infections or some population has a sharp tendency to cause infections when they whether enter the bloodstream or a deep tissue.

Firstly, the scientists combined large-scale population genomics and in vitro measurements of immunologically relevant features of these bacteria. After that, they could utilize ML (machine learning) to successfully predict the risk of developing such life-threatening infections from the genomic features of a bacterial isolate.

The research work of the scientists was published in the journal Nature Communications.  The new development paves the way for future technologies to identify such infections where high-risk genotypes are identified proactively when a person is to undergo a surgical procedure, which has a high potential to reduce the burden of infections caused by S epidermidis.