Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review

• Published in: Bioengineering (Basel) Vol. 9; no. 12; p. 748
• Main Authors: Alshagathrh, Fahad Muflih, Househ, Mowafa Said
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.12.2022; MDPI
• Subjects: Accuracy; Algorithms; Artificial intelligence; Bioengineering; Biopsy; Brain; Coronaviruses; COVID-19; Data management; deep learning; Diagnostic systems; Digital systems; Fatty liver; Feature extraction; Fibrosis; Image acquisition; Inflammation; Literature reviews; Liver; Liver diseases; Machine learning; Magnetic resonance imaging; Medical imaging; Medical research; Methods; NAFLD; Neural networks; Performance enhancement; Qualitative analysis; Steatosis; Support vector machines; Systematic Review; Ultrasonic imaging; Ultrasound; Ultrasound imaging; United States; deep learning; NAFLD; fatty liver; machine learning; artificial intelligence; ultrasound
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering9120748

Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.