Science combined with clinical diagnostics enhances common knowledge to the benefit of the medical community. Explore our findings and stay informed about the latest discoveries.
Introducing LIVERFASt™ in your clinic: Simplifying liver assessment in medicine
Dr. Sam Pappas uses the capabilities of Artificial Intelligence (AI) algorithm LIVERFAStTM blood-based test for evaluation of liver disease, fatty liver disease and NASH (Non Alcoholic Steatohepatitis) to provide excellent care for his patients.
published May 2020
Machine learning technology for evaluation of liver fibrosis, inflammation activity and steatosis (LIVERFASt™)
Use of non-invasive liver tests in extended populations is evaluated in 13068 patients who underwent the LIVERFASt™ test for fatty liver disease assessment. Data evaluation revealed 11% of the patients exhibited significant fibrosis, approximately 7% of the population had severe hepatic inflammation, and steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFASt™ algorithm, NAFLD was detected in 13.41% of the patients.
published April 2020
Assessment of fatty liver disease using a biomarker-based non-invasive algorithm LIVERFASt™ test in South-East Asia
Scientific poster presented at the NASH-TAG conference, January 2020 for “Assessment of fatty liver disease using a biomarker-based non-invasive algorithm LIVERFASt™ test in South-East Asia”
At NASH-TAG international conference, clinicians and researchers share the latest advances and challenges in the diagnosis and therapy of NASH and liver fibrosis.
Non-invasive assessment of liver fibrosis, inflammation and steatosis
The LIVERFASt™ machine learning-based algorithm uses a combination of anthropometric and serum biomarkers that are individually used to provide fatty liver disease staging. It is a reliable, and reproducible tool which provides grading or staging of the three liver lesions: fibrosis, inflammation activity and steatosis.
Non-invasive testing for fatty liver disease for primary care providers
High sensitivity and specificity indicating “Elevated” or “Low” risk for NAFLD can be achieved using a simple algorithm based on minimal information from patients seeking routine check-ups from Primary Care Physicians or PCPs. The algorithmic result is significantly more precise than current reliance on identification of outlier values for standard individual liver function biomarkers. Furthermore, most patients at elevated risk can be evaluated for quantitative SAF score prediction using additional biomarkers - without undergoing expensive or invasive procedures of elastography, imaging or biopsy