Semin Liver Dis 2021; 41(04): 551-556
DOI: 10.1055/s-0041-1731706
Review Article

Artificial Intelligence in Hepatology: A Narrative Review

Karl Vaz
1   Department of Gastroenterology and Hepatology, Austin Health, Melbourne, Australia
,
Thomas Goodwin
2   Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
,
William Kemp
2   Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
3   Central Clinical School, Monash University, Melbourne, Australia
,
Stuart Roberts
2   Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
3   Central Clinical School, Monash University, Melbourne, Australia
,
Ammar Majeed
2   Department of Gastroenterology, The Alfred Hospital, Melbourne, Australia
3   Central Clinical School, Monash University, Melbourne, Australia
› Author Affiliations

Abstract

There has been a tremendous growth in data collection in hepatology over the last decade. This wealth of “big data” lends itself to the application of artificial intelligence in the development of predictive and diagnostic models with potentially greater accuracy than standard biostatistics. As processing power of computing systems has improved and data are made more accessible through the large databases and electronic health record, these more contemporary techniques for analyzing and interpreting data have garnered much interest in the field of medicine. This review highlights the current evidence base for the use of artificial intelligence in hepatology, focusing particularly on the areas of diagnosis and prognosis of advanced chronic liver disease and hepatic neoplasia.

Author Contributions

All authors contributed to the drafting and/or review of the paper.


Supplementary Material



Publication History

Article published online:
29 July 2021

© 2021. Thieme. All rights reserved.

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  • References

  • 1 Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol 2019; 28 (02) 73-81
  • 2 Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380 (14) 1347-1358
  • 3 Le Berre C, Sandborn WJ, Aridhi S. et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020; 158 (01) 76-94.e2
  • 4 Deo RC. Machine learning in medicine. Circulation 2015; 132 (20) 1920-1930
  • 5 Beam AL, Kohane IS. Big data and machine learning in health care. JAMA 2018; 319 (13) 1317-1318
  • 6 Chicco D. Ten quick tips for machine learning in computational biology. BioData Min 2017; 10: 35
  • 7 Tan AC, Gilbert D. An empirical comparison of supervised machine learning techniques in bioinformatics. APBC 2003; 19: 219-222 DOI: 10.5555/820189.820218.
  • 8 Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H. eDoctor: machine learning and the future of medicine. J Intern Med 2018; 284 (06) 603-619
  • 9 Tapper EB, Lok ASF. Use of liver imaging and biopsy in clinical practice. N Engl J Med 2017; 377 (08) 756-768
  • 10 Piscaglia F, Cucchetti A, Benlloch S. et al. Prediction of significant fibrosis in hepatitis C virus infected liver transplant recipients by artificial neural network analysis of clinical factors. Eur J Gastroenterol Hepatol 2006; 18 (12) 1255-1261
  • 11 Benlloch S, Berenguer M, Prieto M, Rayón JM, Aguilera V, Berenguer J. Prediction of fibrosis in HCV-infected liver transplant recipients with a simple noninvasive index. Liver Transpl 2005; 11 (04) 456-462
  • 12 Hashem S, Esmat G, Elakel W. et al. Comparison of machine learning approaches for prediction of advanced liver fibrosis in chronic hepatitis C patients. IEEE/ACM Trans Comput Biol Bioinform 2018; 15 (03) 861-868
  • 13 Haydon GH, Jalan R, Ala-Korpela M. et al. Prediction of cirrhosis in patients with chronic hepatitis C infection by artificial neural network analysis of virus and clinical factors. J Viral Hepat 1998; 5 (04) 255-264
  • 14 Stoean R, Stoean C, Lupsor M, Stefanescu H, Badea R. Evolutionary-driven support vector machines for determining the degree of liver fibrosis in chronic hepatitis C. Artif Intell Med 2011; 51 (01) 53-65
  • 15 Wang D, Wang Q, Shan F, Liu B, Lu C. Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect Dis 2010; 10: 251
  • 16 Chen Y, Luo Y, Huang W. et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Comput Biol Med 2017; 89: 18-23
  • 17 Wang K, Lu X, Zhou H. et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study. Gut 2019; 68 (04) 729-741
  • 18 Raoufy MR, Vahdani P, Alavian SM, Fekri S, Eftekhari P, Gharibzadeh S. A novel method for diagnosing cirrhosis in patients with chronic hepatitis B: artificial neural network approach. J Med Syst 2011; 35 (01) 121-126
  • 19 Cao Y, Hu ZD, Liu XF, Deng AM, Hu CJ. An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters. Dis Markers 2013; 35 (06) 653-660
  • 20 Sowa JP, Heider D, Bechmann LP, Gerken G, Hoffmann D, Canbay A. Novel algorithm for non-invasive assessment of fibrosis in NAFLD. PLoS One 2013; 8 (04) e62439
  • 21 Pournik O, Dorri S, Zabolinezhad H, Alavian SM, Eslami S. 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
  • 22 Owjimehr M, Danyali H, Helfroush MS, Shakibafard A. Staging of fatty liver diseases based on hierarchical classification and feature fusion for back-scan-converted ultrasound images. Ultrason Imaging 2017; 39 (02) 79-95
  • 23 Perakakis N, Polyzos SA, Yazdani A. et al. Non-invasive diagnosis of non-alcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study. Metabolism 2019; 101: 154005
  • 24 Gatos I, Tsantis S, Spiliopoulos S. et al. A machine-learning algorithm toward color analysis for chronic liver disease classification, employing ultrasound shear wave elastography. Ultrasound Med Biol 2017; 43 (09) 1797-1810
  • 25 Banerjee R, Das A, Ghoshal UC, Sinha M. Predicting mortality in patients with cirrhosis of liver with application of neural network technology. J Gastroenterol Hepatol 2003; 18 (09) 1054-1060
  • 26 Lapuerta P, Rajan S, Bonacini M. Neural networks as predictors of outcomes in alcoholic patients with severe liver disease. Hepatology 1997; 25 (02) 302-306
  • 27 Eaton JE, Vesterhus M, McCauley BM. et al. Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTO) predicts outcomes in PSC: a derivation & validation study using machine learning. Hepatology 2020; 71 (01) 214-224
  • 28 Konerman MA, Zhang Y, Zhu J, Higgins PDR, Lok ASF, Waljee AK. Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data. Hepatology 2015; 61 (06) 1832-1841
  • 29 Yasaka K, Akai H, Abe O, Kiryu S. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018; 286 (03) 887-896
  • 30 Guo LH, Wang D, Qian YY. et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images. Clin Hemorheol Microcirc 2018; 69 (03) 343-354
  • 31 Hassan TM, Elmogy M, Sallam ES. Diagnosis of focal liver diseases based on deep learning technique for ultrasound images. Arab J Sci Eng 2017; 42 (08) 3127-3140
  • 32 Ben-Cohen A, Klang E, Diamant I. et al. CT image-based decision support system for categorization of liver metastases into primary cancer sites. Acad Radiol 2017; 24 (12) 1501-1509
  • 33 Preis O, Blake MA, Scott JA. Neural network evaluation of PET scans of the liver: a potentially useful adjunct in clinical interpretation. Radiology 2011; 258 (03) 714-721
  • 34 Abajian A, Murali N, Savic LJ. et al. Predicting treatment response to intra-arterial therapies of hepatocellular carcinoma using supervised machine learning—an artificial intelligence concept. J Vasc Interv Radiol 2018; 29 (06) 850-857.e1
  • 35 Rinella ME, Tacke F, Sanyal AJ, Anstee QM. participants of the AASLD/EASL Workshop. Report on the AASLD/EASL joint workshop on clinical trial endpoints in NAFLD. Hepatology 2019; 70 (04) 1424-1436
  • 36 Kleiner DE, Brunt EM, Van Natta M. et al; Nonalcoholic Steatohepatitis Clinical Research Network. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005; 41 (06) 1313-1321
  • 37 Gawrieh S, Knoedler DM, Saeian K, Wallace JR, Komorowski RA. Effects of interventions on intra- and interobserver agreement on interpretation of nonalcoholic fatty liver disease histology. Ann Diagn Pathol 2011; 15 (01) 19-24
  • 38 Vanderbeck S, Bockhorst J, Kleiner D, Komorowski R, Chalasani N, Gawrieh S. Automatic quantification of lobular inflammation and hepatocyte ballooning in nonalcoholic fatty liver disease liver biopsies. Hum Pathol 2015; 46 (05) 767-775
  • 39 Forlano R, Mullish BH, Giannakeas N. et al. High-throughput, machine learning-based quantification of steatosis, inflammation, ballooning, and fibrosis in biopsies from patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol 2020; 18 (09) 2081-2090.e9
  • 40 Gawrieh S, Sethunath D, Cummings OW. et al. Automated quantification and architectural pattern detection of hepatic fibrosis in NAFLD. Ann Diagn Pathol 2020; 47: 151518
  • 41 Taylor-Weiner A, Pokkalla H, Han L, Jia C. et al. A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH. Hepatology 2021; (e-pub ahead of print). DOI: 10.1002/hep.31750.
  • 42 Wong GLH, Yuen PC, Ma AJ, Chan AWH, Leung HHW, Wong VWS. Artificial intelligence in prediction of non-alcoholic fatty liver disease and fibrosis. J Gastroenterol Hepatol 2021; 36 (03) 543-550
  • 43 Watson DS, Krutzinna J, Bruce IN. et al. Clinical applications of machine learning algorithms: beyond the black box. BMJ 2019; 364: l886
  • 44 Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019; 1: 206-215
  • 45 Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med 2018; 178 (11) 1544-1547
  • 46 Rajkomar A, Hardt M, Howell MD, Corrado G, Chin MH. Ensuring fairness in machine learning to advance health equity. Ann Intern Med 2018; 169 (12) 866-872
  • 47 Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med 2018; 378 (11) 981-983
  • 48 Keskinbora KH. Medical ethics considerations on artificial intelligence. J Clin Neurosci 2019; 64: 277-282