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Abstract
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 Fibrosis) scores obtained using the LIVERFAStTM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay 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 LIVERFAStTM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians. This system may lead to the use of noninvasive liver tests in extended populations for more accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and provision of an efficient system for therapeutic interventions.
Highlights
Artificial intelligence (AI), deep learning algorithms, is gaining extensive attention for its excellent performance in liver disease-recognition tasks [1] [2] [3] [4]
It is expected that 20% - 30% of those with newly detected nonalcoholic fatty liver disease (NAFLD) will have already progressed to nonalcoholic steatohepatitis (NASH); and among these, 10% - 20% will progress to cirrhosis and/or hepatocellular carcinoma [8] [9] [10] [11]
We evaluated the LIVERFAStTM technology on its specificity (Sp), sensitivity (Se), positive predictive value (PPV) and negative predictive value (NPV)
Summary
Artificial intelligence (AI), deep learning algorithms, is gaining extensive attention for its excellent performance in liver disease-recognition tasks [1] [2] [3] [4]. Nonalcoholic steatohepatitis (NASH) is currently the second leading cause of liver disease among those awaiting liver transplantation in the United States [8]. Among patients with T2DM, the prevalence of NAFLD may be as high as 70% and is often associated with cardiovascular disease [12]. Current practice guidelines fail to support routine screening for NAFLD/NASH in patients with T2DM due in part to the difficulty in properly diagnosing the disease in high-risk groups [7]
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