Application of artificial intelligence in non-alcoholic fatty liver disease and liver fibrosis: a systematic review and meta-analysis

1. Asrani, SK, Devarbhavi, H, Eaton, J, et al. Burden of liver diseases in the world. J Hepatol 2019; 70: 151–171.
Google Scholar | Crossref | Medline2. Goldberg, D, Ditah, IC, Saeian, K, et al. Changes in the prevalence of hepatitis C virus infection, nonalcoholic steatohepatitis, and alcoholic liver disease among patients with cirrhosis or liver failure on the waitlist for liver transplantation. Gastroenterology 2017; 152: 1090–1099.
Google Scholar | Crossref | Medline3. Estes, C, Razavi, H, Loomba, R, et al. Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease. Hepatology 2018; 67: 123–133.
Google Scholar | Crossref | Medline4. Younossi, Z, Tacke, F, Arrese, M, et al. Global perspectives on nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatology 2019; 69: 2672–2682.
Google Scholar | Crossref | Medline5. Eslam, M, Newsome, PN, Sarin, SK, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol 2020; 73: 202–209.
Google Scholar | Crossref | Medline6. Eslam, M, Sanyal, AJ, George, J, et al. MAFLD: a consensus-driven proposed nomenclature for metabolic associated fatty liver disease. Gastroenterology 2020; 158: 1999–2014.
Google Scholar | Crossref | Medline7. Piccinino, F, Sagnelli, E, Pasquale, G, et al. Complications following percutaneous liver biopsy. A multicentre retrospective study on 68,276 biopsies. J Hepatol 1986; 2: 165–173.
Google Scholar | Crossref | Medline | ISI8. Kotronen, A, Peltonen, M, Hakkarainen, A, et al. Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. Gastroenterology 2009; 137: 865–872.
Google Scholar | Crossref | Medline | ISI9. Saadeh, S, Younossi, ZM, Remer, EM, et al. The utility of radiological imaging in nonalcoholic fatty liver disease. Gastroenterology 2002; 123: 745–750.
Google Scholar | Crossref | Medline | ISI10. Middleton, MS, Van Natta, ML, Heba, ER, et al. Diagnostic accuracy of magnetic resonance imaging hepatic proton density fat fraction in pediatric nonalcoholic fatty liver disease. Hepatology 2018; 67: 858–872.
Google Scholar | Crossref | Medline11. Besutti, G, Valenti, L, Ligabue, G, et al. Accuracy of imaging methods for steatohepatitis diagnosis in non-alcoholic fatty liver disease patients: a systematic review. Liver Int 2019; 39: 1521–1534.
Google Scholar | Crossref | Medline12. Xiao, G, Zhu, S, Xiao, X, et al. Comparison of laboratory tests, ultrasound, or magnetic resonance elastography to detect fibrosis in patients with nonalcoholic fatty liver disease: a meta-analysis. Hepatology 2017; 66: 1486–1501.
Google Scholar | Crossref | Medline13. Le Berre, C, Sandborn, WJ, Aridhi, S, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020; 158: 76–94.e72.
Google Scholar | Crossref14. Spann, A, Yasodhara, A, Kang, J, et al. Applying machine learning in liver disease and transplantation: a comprehensive review. Hepatology 2020; 71: 1093–1105.
Google Scholar | Crossref | Medline15. Popa, SL, Ismaiel, A, Cristina, P, et al. Non-alcoholic fatty liver disease: implementing complete automated diagnosis and staging. A systematic review. Diagnostics 2021; 11: 1078.
Google Scholar | Crossref16. Decharatanachart, P, Chaiteerakij, R, Tiyarattanachai, T, et al. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis. BMC Gastroenterol 2021; 21: 10.
Google Scholar | Crossref | Medline17. Page, MJ, McKenzie, JE, Bossuyt, PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.
Google Scholar | Crossref | Medline18. Whiting, PF, Rutjes, AW, Westwood, ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155: 529–536.
Google Scholar | Crossref | Medline | ISI19. Review Manager (RevMan) [Computer program]. Version 5.3 ed. Copenhagen: The Nordic Cochrane Centre, the Cochrane Collaboration, 2014.
Google Scholar20. R: A language environment for statistical computing . Vienna: R Core Team, 2019.
Google Scholar21. Gallego-Duran, R, Cerro-Salido, P, Gomez-Gonzalez, E, et al. Imaging biomarkers for steatohepatitis and fibrosis detection in non-alcoholic fatty liver disease. Sci Rep 2016; 6: 31421.
Google Scholar | Crossref | Medline22. Kuppili, V, Biswas, M, Sreekumar, A, et al. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization. J Med Syst 2017; 41: 152.
Google Scholar | Crossref | Medline23. Byra, M, Styczynski, G, Szmigielski, C, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. Int J Comput Assist Radiol Surg 2018; 13: 1895–1903.
Google Scholar | Crossref | Medline24. Biswas, M, Kuppili, V, Edla, DR, et al. Symtosis: a liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm. Comput Methods Programs Biomed 2018; 155: 165–177.
Google Scholar | Crossref | Medline25. Shi, X, Ye, W, Liu, F, et al. Ultrasonic liver steatosis quantification by a learning-based acoustic model from a novel shear wave sequence. Biomed Eng Online 2019; 18: 121.
Google Scholar | Crossref | Medline26. Han, A, Byra, M, Heba, E, et al. Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology 2020; 295: 342–350.
Google Scholar | Crossref | Medline27. Zamanian, H, Mostaar, A, Azadeh, P, et al. Implementation of combinational deep learning algorithm for non-alcoholic fatty liver classification in ultrasound images. J Biomed Phys Eng 2021; 11: 73–84.
Google Scholar | Crossref | Medline28. Ma, H, Xu, CF, Shen, Z, et al. Application of machine learning techniques for clinical predictive modeling: a cross-sectional study on nonalcoholic fatty liver disease in China. Biomed Res Int 2018; 2018: 4304376.
Google Scholar | Crossref | Medline29. Islam, MM, Wu, CC, Poly, TN, et al. Applications of machine learning in fatty live disease prediction. Stud Health Technol Inform 2018; 247: 166–170.
Google Scholar | Medline30. Wu, CC, Yeh, WC, Hsu, WD, et al. Prediction of fatty liver disease using machine learning algorithms. Comput Methods Programs Biomed 2019; 170: 23–29.
Google Scholar | Crossref | Medline31. Atabaki-Pasdar, N, Ohlsson, M, Viñuela, A, et al. Predicting and elucidating the etiology of fatty liver disease: a machine learning modeling and validation study in the IMI DIRECT cohorts. PLoS Med 2020; 17: e1003149.
Google Scholar | Crossref | Medline32. Chen, YS, Chen, D, Shen, C, et al. A novel model for predicting fatty liver disease by means of an artificial neural network. Gastroenterol Rep (Oxf) 2021; 9: 31–37.
Google Scholar | Crossref | Medline33. Liu, YX, Liu, X, Cen, C, et al. Comparison and development of advanced machine learning tools to predict nonalcoholic fatty liver disease: an extended study. Hepatobiliary Pancreat Dis Int 2021; 20: 409–415.
Google Scholar | Crossref | Medline34. Naganawa, S, Enooku, K, Tateishi, R, et al. Imaging prediction of nonalcoholic steatohepatitis using computed tomography texture analysis. Eur Radiol 2018; 28: 3050–3058.
Google Scholar | Crossref | Medline35. Uehara, D, Hayashi, Y, Seki, Y, et al. Non-invasive prediction of non-alcoholic steatohepatitis in Japanese patients with morbid obesity by artificial intelligence using rule extraction technology. World J Hepatol 2018; 10: 934–943.
Google Scholar | Crossref | Medline36. Garcia-Carretero, R, Vigil-Medina, L, Barquero-Perez, O, et al. Relevant features in nonalcoholic steatohepatitis determined using machine learning for feature selection. Metab Syndr Relat Disord 2019; 17: 444–451.
Google Scholar | Crossref | Medline37. Docherty, M, Regnier, SA, Capkun, G, et al. Development of a novel machine learning model to predict presence of nonalcoholic steatohepatitis. J Am Med Inform Assoc 2021; 28: 1235–1241.
Google Scholar | Crossref | Medline38. Pournik, O, Dorri, S, Zabolinezhad, H, et al. A diagnostic model for cirrhosis in patients with non-alcoholic fatty liver disease: an artificial neural network approach. Med J Islam Repub Iran 2014; 28: 116.
Google Scholar | Medline39. Shahabi, M, Hassanpour, H, Mashayekhi, H. Rule extraction for fatty liver detection using neural networks. Neur Comput Appl 2019; 31: 979–989.
Google Scholar | Crossref40. Okanoue, T, Shima, T, Mitsumoto, Y, et al. Artificial intelligence/neural network system for the screening of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis. Hepatol Res 2021; 51: 554–569.
Google Scholar | Crossref | Medline41. Okanoue, T, Shima, T, Mitsumoto, Y, et al. Novel artificial intelligent/neural network system for staging of nonalcoholic steatohepatitis. Hepatol Res 2021; 51: 1044–1057.
Google Scholar | Crossref | Medline42. Vanderbeck, S, Bockhorst, J, Komorowski, R, et al. Automatic classification of white regions in liver biopsies by supervised machine learning. Hum Pathol 2014; 45: 785–792.
Google Scholar | Crossref | Medline43. Liu, F, Goh, GB, Tiniakos, D, et al. qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis. Hepatology 2020; 71: 1953–1966.
Google Scholar | Crossref | Medline44. Sun, L, Marsh, JN, Matlock, MK, et al. Deep learning quantification of percent steatosis in donor liver biopsy frozen sections. Ebiomedicine 2020; 60: 103029.
Google Scholar | Crossref | Medline45. Teramoto, T, Shinohara, T, Takiyama, A. Computer-aided classification of hepatocellular ballooning in liver biopsies from patients with NASH using persistent homology. Comput Methods Programs Biomed 2020; 195: 105614.
Google Scholar | Crossref | Medline46. Matteoni, CA, Younossi, ZM, Gramlich, T, et al. Nonalcoholic fatty liver disease: a spectrum of clinical and pathological severity. Gastroenterology 1999; 116: 1413–1419.
Google Scholar | Crossref | Medline | ISI47. Karlas, T, Petroff, D, Sasso, M, et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol 2017; 66: 1022–1030.
Google Scholar | Crossref | Medline48. Lee, SS, Park, SH, Kim, HJ, et al. Non-invasive assessment of hepatic steatosis: prospective comparison of the accuracy of imaging examinations. J Hepatol 2010; 52: 579–585.
Google Scholar | Crossref | Medline49. Intraobserver interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. The French METAVIR Cooperative Study Group . Hepatology 1994; 20: 15–20.
Google Scholar | Crossref | Medline50. Theodossi, A, Skene, AM, Portmann, B, et al. Observer variation in assessment of liver biopsies including analysis by kappa statistics. Gastroenterology 1980; 79: 232–241.

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