Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFASt<sup>TM</sup>)

Abstract

Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFASt TM 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 LIVERFASt TM 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 LIVERFASt TM 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 Fibrosis) scores obtained using the LIVERFASt TM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay < 2, Fz > 0). Approximately 1.91% (Sx > 0, Ay = 2, Fz > 0) of the patients showed NAFLD or NASH scorings while 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1-2) and 1.49% had advanced NASH (Sx > 0, Ay > 2, Fz = 3-4). The modified SAF scoring system generated by LIVERFASt TM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians.

Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters

“…The findings were confirmed by a database containing pathological assessments. Aravind used clinical data from a collection of candidates to develop a machine-learning model to estimate the SAF score based on steatosis, activity score, and fibrosis assessments 14 . The employed model was a multilayer perceptron model (MLP), trained on 16 different datasets.…”