Our algorithm identifies AMR risk by analyzing vast datasets to detect resistance patterns and predict future trends. By providing early, accurate insights, it enables timely interventions and informed treatment decisions, thereby preventing the spread of resistant infections and preserving the effectiveness of antibiotics. This proactive approach helps protect the future of global health by ensuring effective infection control and sustainable antimicrobial use.
Traditional models of assessing AMR risk rely on manual laboratory tests and clinical observations, which can be time-consuming and subject to human error. These methods often involve culturing bacteria and testing their response to antibiotics, providing results that may take days. Additionally, traditional models may lack the ability to analyze large datasets or predict future trends, limiting their effectiveness in early detection and proactive intervention.
Our Ditekta Risk technology revolutionizes AMR risk assessment by leveraging advanced AI and machine learning to analyze large datasets in real-time, offering rapid and precise detection of resistance patterns. This enables early intervention, accurate predictions of future trends, and informed treatment decisions, significantly enhancing the effectiveness of AMR management and prevention efforts.
Our Profound Risk solution will provides precise and consistent risk assessments using validated methodologies and robust data analysis, crucial for making informed decisions.
It will offer real-time data analysis and immediate reporting of risk levels, enabling timely interventions and effective management of emerging threats.
a solution with advanced predictive analytics to anticipate future trends and potential risks, allowing proactive measures to mitigate the impact of antimicrobial resistance.
Diversity matters within AI training and testing datasets. The ProFound Risk AMR Solution's deep learning dataset includes racial, ethnic, and geographic diversity within its AI research and training. This diversity not only increases our unique algorithm's generalizability but also ensures clinical effectiveness across varied populations and environments. By encompassing a wide range of genetic backgrounds and healthcare practices, our technology provides accurate and equitable AMR risk assessments, leading to better-informed treatment decisions and improved health outcomes for all communities.
Using a US nationwide survey, disparities in antimicrobial drug acquisition by race/ethnicity for 2014–2015 was measured. White persons reported twice as many antimicrobial drug prescription fills per capita as persons of other race/ethnicities. Characterizing antimicrobial drug use by demographic might improve antimicrobial drug stewardship and help address antimicrobial drug resistance.
