Abstract
BackgroundThe gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Various noninvasive modalities, e.g., ultrasonography, elastography and clinical predictive scores, have been used as alternatives to liver biopsy, with limited performance. Recently, artificial intelligence (AI) models have been developed and integrated into noninvasive diagnostic tools to improve their performance.MethodsWe systematically searched for studies on AI-assisted diagnosis of liver fibrosis and NAFLD on MEDLINE, Scopus, Web of Science and Google Scholar. The pooled sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic odds ratio (DOR) with their 95% confidence intervals (95% CIs) were calculated using a random effects model. A summary receiver operating characteristic curve and the area under the curve was generated to determine the diagnostic accuracy of the AI-assisted system. Subgroup analyses by diagnostic modalities, population and AI classifiers were performed.ResultsWe included 19 studies reporting the performances of AI-assisted ultrasonography, elastrography, computed tomography, magnetic resonance imaging and clinical parameters for the diagnosis of liver fibrosis and steatosis. For the diagnosis of liver fibrosis, the pooled sensitivity, specificity, PPV, NPV and DOR were 0.78 (0.71–0.85), 0.89 (0.81–0.94), 0.72 (0.58–0.83), 0.92 (0.88–0.94) and 31.58 (11.84–84.25), respectively, for cirrhosis; 0.86 (0.80–0.90), 0.87 (0.80–0.92), 0.85 (0.75–0.91), 0.88 (0.82–0.92) and 37.79 (16.01–89.19), respectively; for advanced fibrosis; and 0.86 (0.78–0.92), 0.81 (0.77–0.84), 0.88 (0.80–0.93), 0.77 (0.58–0.89) and 26.79 (14.47–49.62), respectively, for significant fibrosis. Subgroup analyses showed significant differences in performance for the diagnosis of fibrosis among different modalities. The pooled sensitivity, specificity, PPV, NPV and DOR were 0.97 (0.76–1.00), 0.91 (0.78–0.97), 0.95 (0.87–0.98), 0.93 (0.80–0.98) and 191.52 (38.82–944.81), respectively, for the diagnosis of liver steatosis.ConclusionsAI-assisted systems have promising potential for the diagnosis of liver fibrosis and NAFLD. Validations of their performances are warranted before implementing these AI-assisted systems in clinical practice.Trial registration: The protocol was registered with PROSPERO (CRD42020183295).
Highlights
The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy
Articles were excluded for the following reasons: studies that were duplicated (n = 149), studies that were conducted in animals (n = 10), studies focusing on diseases other than liver parenchymal diseases (n = 11), studies that were not original research, i.e., reviews, editorials (n = 35), studies that were not written
We found that the pooled sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 0.86, 0.81, 0.88 and 0.77, respectively, and the diagnostic odds ratio (DOR) was 26.79
Summary
The gold standard for the diagnosis of liver fibrosis and nonalcoholic fatty liver disease (NAFLD) is liver biopsy. Chronic liver diseases and cirrhosis are the 11th leading cause of death in the world, accounting for 1.1 million deaths annually [1]. Common causes of cirrhosis are chronic hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, alcohol-related liver disease and nonalcoholic steatohepatitis (NASH) [2]. The estimated worldwide prevalence of nonalcoholic fatty liver disease (NAFLD) is 25% [4] and is projected to be to 33.5% by 2030, emphasizing the importance of both cirrhosis and NAFLD [5]. Without a prompt diagnosis and proper treatments, it can quickly deteriorate to decompensated cirrhosis, which eventually leads to complications and mortality. The detection and treatment of early-stage fibrosis and NASH can slow disease progression, reduce the risk of liver cancer and decrease mortality
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