Non-alcoholic fatty liver disease(NAFLD)and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide.It is critical to develop reliable methods of predicting progression to fi...Non-alcoholic fatty liver disease(NAFLD)and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide.It is critical to develop reliable methods of predicting progression to fibrosis,cirrhosis,and decompensated liver disease.Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data.Artificial intelligence(AI)provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis,and to identify patients at high risk for disease progression.We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis.Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis.We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction(MELD-Na score,liver biopsy,FIB-4 score,and biomarkers).They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy,for which there are currently no established prediction tools.These studies show that AI could be a helpful adjunct to existing techniques for diagnosing,monitoring,and treating both NAFLD and viral hepatitis.展开更多
文摘Non-alcoholic fatty liver disease(NAFLD)and chronic viral hepatitis are among the most significant causes of liver-related mortality worldwide.It is critical to develop reliable methods of predicting progression to fibrosis,cirrhosis,and decompensated liver disease.Current screening methods such as biopsy and transient elastography are limited by invasiveness and observer variation in analysis of data.Artificial intelligence(AI)provides a unique opportunity to more accurately diagnose NAFLD and viral hepatitis,and to identify patients at high risk for disease progression.We conducted a literature review of existing evidence for AI in NAFLD and viral hepatitis.Thirteen articles on AI in NAFLD and 14 on viral hepatitis were included in our analysis.We found that machine learning algorithms were comparable in accuracy to current methods for diagnosis and fibrosis prediction(MELD-Na score,liver biopsy,FIB-4 score,and biomarkers).They also reliably predicted hepatitis C treatment failure and hepatic encephalopathy,for which there are currently no established prediction tools.These studies show that AI could be a helpful adjunct to existing techniques for diagnosing,monitoring,and treating both NAFLD and viral hepatitis.
