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Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFAStTM)

DOI: 10.4236/jilsa.2020.122003, PP. 31-49

Keywords: Machine Learning (ML), Artificial Intelligence (AI), Neural Networks (NNs), Steatosis, Inflammation Activity, Fibrosis (SAF Score), Nonalcoholic Fatty Liver Disease (NAFLD), Non-Alcoholic Steatohepatitis (NASH)

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Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFAStTM has been used to evaluate the diagnostic accuracy of machine learning (ML) biomarker algorithms to assess liver damage. Prevalence of NAFLD (Nonalcoholic fatty liver disease) and resulting NASH (nonalcoholic steatohepatitis) are constantly increasing worldwide, creating challenges for screening as the diagnosis for NASH requires invasive liver biopsy. Key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. In this prospective study, the staging of three different lesions of the liver to diagnose fatty liver was analyzed using a proprietary ML algorithm LIVERFAStTM developed with a database of 2862 unique medical assessments of biomarkers, where 1027 assessments were used to train the algorithm and 1835 constituted the validation set. Data of 13,068 patients who underwent the LIVERFAStTM test for evaluation of fatty liver disease were analysed. Data evaluation revealed 11% of the patients exhibited significant fibrosis with fibrosis scores 0.6 - 1.00. Approximately 7% of the population had severe hepatic inflammation. Steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and


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