Back
 JILSA  Vol.12 No.2 , May 2020
Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFAStTM)
Abstract: Using the latest available artificial intelligence (AI) technology, an advanced algorithm LIVERFAStTM has been used to evaluate the diagnostic accuracy of machine learning (ML) biomarker algorithms to assess liver damage. Prevalence of NAFLD (Nonalcoholic fatty liver disease) and resulting NASH (nonalcoholic steatohepatitis) are constantly increasing worldwide, creating challenges for screening as the diagnosis for NASH requires invasive liver biopsy. Key issues in NAFLD patients are the differentiation of NASH from simple steatosis and identification of advanced hepatic fibrosis. In this prospective study, the staging of three different lesions of the liver to diagnose fatty liver was analyzed using a proprietary ML algorithm LIVERFAStTM developed with a database of 2862 unique medical assessments of biomarkers, where 1027 assessments were used to train the algorithm and 1835 constituted the validation set. Data of 13,068 patients who underwent the LIVERFAStTM test for evaluation of fatty liver disease were analysed. Data evaluation revealed 11% of the patients exhibited significant fibrosis with fibrosis scores 0.6 - 1.00. Approximately 7% of the population had severe hepatic inflammation. Steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFAStTM algorithm, NAFLD was detected in 13.41% of the patients (Sx > 0, Ay < 2, Fz > 0). Approximately 1.91% (Sx > 0, Ay = 2, Fz > 0) of the patients showed NAFLD or NASH scorings while 1.08% had confirmed NASH (Sx > 0, Ay > 2, Fz = 1 - 2) and 1.49% had advanced NASH (Sx > 0, Ay > 2, Fz = 3 - 4). The modified SAF scoring system generated by LIVERFAStTM provides a simple and convenient evaluation of NAFLD and NASH in a cohort of Southeast Asians. This system may lead to the use of noninvasive liver tests in extended populations for more accurate diagnosis of liver pathology, prediction of clinical path of individuals at all stages of liver diseases, and provision of an efficient system for therapeutic interventions.
Cite this paper: Aravind, A. , Bahirvani, A. , Quiambao, R. and Gonzalo, T. (2020) Machine Learning Technology for Evaluation of Liver Fibrosis, Inflammation Activity and Steatosis (LIVERFAStTM). Journal of Intelligent Learning Systems and Applications, 12, 31-49. doi: 10.4236/jilsa.2020.122003.
References

[1]   Wei, W., Wu, X., Zhou, J., Sun, Y., Kong, Y. and Yang, X. (2019) Noninvasive Evaluation of Liver Fibrosis Reverse Using Artificial Neural Network Model for Chronic Hepatitis B Patients. Computational and Mathematical Methods in Medicine, 2019, 7239780-7239788.
https://doi.org/10.1155/2019/7239780

[2]   Chang, N.-W., et al. (2017) Biomarker Identification of Hepatocellular Carcinoma Using a Methodical Literature Mining Strategy. Database (Oxford), 2017, bax082.
https://doi.org/10.1093/database/bax082

[3]   Li, B., et al. (2017) Artificial Neural Network Models for Early Diagnosis of Hepatocellular Carcinoma Using Serum Levels of α-Fetoprotein, α-Fetoprotein-L3, Des-γ-Carboxy prothrombin and Golgi Protein 73. Oncotarget, 8, 80521-80530.
https://doi.org/10.18632/oncotarget.19298

[4]   Choi, K.J., et al. (2018) Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-Enhanced CT Images in the Liver. Radiology, 289, 688-697.
https://doi.org/10.1148/radiol.2018180763

[5]   Huang, Q., Zhang, F. and Li, X. (2018) Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey. BioMed Research International, 2018, Article ID: 5137904.
https://www.hindawi.com/journals/bmri/2018/5137904/
https://doi.org/10.1155/2018/5137904


[6]   Younossi, Z.M., Koenig, A.B., Abdelatif, D., Fazel, Y., Henry, L. and Wymer, M. (2016) Global Epidemiology of Nonalcoholic Fatty Liver Disease-Meta-Analytic Assessment of Prevalence, Incidence and Outcomes. Hepatology, 64, 73-84.
https://doi.org/10.1002/hep.28431

[7]   Chalasani, N., et al. (2012) The Diagnosis and Management of Non-Alcoholic Fatty Liver Disease: Practice Guideline by the American Association for the Study of Liver Diseases, American College of Gastroenterology and the American Gastroenterological Association. Hepatology, 55, 2005-2023.
https://doi.org/10.1002/hep.25762

[8]   Wong, R.J., et al. (2015) Nonalcoholic Steatohepatitis Is the Second Leading Etiology of Liver Disease among Adults Awaiting Liver Transplantation in the United States. Gastroenterology, 148, 547-555.
https://doi.org/10.1053/j.gastro.2014.11.039

[9]   Ascha, M.S., Hanouneh, I.A., Lopez, R., Tamimi, T.A.-R., Feldstein, A.F. and Zein, N.N. (2010) The Incidence and Risk Factors of Hepatocellular Carcinoma in Patients with Nonalcoholic Steatohepatitis. Hepatology, 51, 1972-1978.
https://doi.org/10.1002/hep.23527

[10]   Bhala, N., et al. (2011) The Natural History of Nonalcoholic Fatty Liver Disease with Advanced Fibrosis or Cirrhosis: An International Collaborative Study. Hepatology, 54, 1208-1216.
https://doi.org/10.1002/hep.24491

[11]   Wong, V.W.-S., et al. (2010) Disease Progression of Non-Alcoholic Fatty Liver Disease: A Prospective Study with Paired Liver Biopsies at 3 Years. Gut, 59, 969-974.
https://doi.org/10.1136/gut.2009.205088

[12]   Targher, G., et al. (2007) Prevalence of Nonalcoholic Fatty Liver Disease and Its Association with Cardiovascular Disease among Type 2 Diabetic Patients. Diabetes Care, 30, 1212-1218.
https://doi.org/10.2337/dc06-2247

[13]   Chalasani, N., et al. (2018) The Diagnosis and Management of Nonalcoholic Fatty Liver Disease: Practice Guidance from the American Association for the Study of Liver Diseases. Hepatology, 67, 328-357.
https://doi.org/10.1002/hep.29367

[14]   European Association for the Study of the Liver (EASL), European Association for the Study of Diabetes (EASD) and European Association for the Study of Obesity (EASO) (2016) EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease. Journal of Hepatology, 64, 1388-1402.
https://doi.org/10.1016/j.jhep.2015.11.004

[15]   Kleiner, D. E., et al. (2005) Design and Validation of a Histological Scoring System for Nonalcoholic Fatty Liver Disease. Hepatology, 41, 1313-1321.
https://doi.org/10.1002/hep.20701

[16]   Shiha, G., et al. (2009) Liver Fibrosis: Consensus Recommendations of the Asian Pacific Association for the Study of the Liver (APASL). Hepatology International, 3, 323-333.
https://doi.org/10.1007/s12072-008-9114-x

[17]   Bedossa, P., et al. (2012) Histopathological Algorithm and Scoring System for Evaluation of Liver Lesions in Morbidly Obese Patients. Hepatology, 56, 1751-1759.
https://doi.org/10.1002/hep.25889

[18]   Sumida, Y., Nakajima, A. and Itoh, Y. (2014) Limitations of Liver Biopsy and Non-Invasive Diagnostic Tests for the Diagnosis of Nonalcoholic Fatty Liver Disease/Nonalcoholic Steatohepatitis. World Journal of Gastroenterology, 20, 475-485.
https://doi.org/10.3748/wjg.v20.i2.475

[19]   Janiec, D.J., Jacobson, E.R., Freeth, A., Spaulding, L. and Blaszyk, H. (2005) Histologic Variation of Grade and Stage of Non-Alcoholic Fatty Liver Disease in Liver Biopsies. Obesity Surgery, 15, 497-501.
https://doi.org/10.1381/0960892053723268

[20]   Ratziu, V., et al. (2005) Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease. Gastroenterology, 128, 1898-1906.
https://doi.org/10.1053/j.gastro.2005.03.084

[21]   Rousselet, M.-C., et al. (2005) Sources of Variability in Histological Scoring of Chronic Viral Hepatitis. Hepatology, 41, 257-264.
https://doi.org/10.1002/hep.20535

[22]   Pandyarajan, V., Gish, R.G., Alkhouri, N. and Noureddin, M. (2019) Screening for Nonalcoholic Fatty Liver Disease in the Primary Care Clinic. Gastroenterology & Hepatology, 15, 357-365.

[23]   WHO (2014) Guidelines for the Screening, Care and Treatment of Persons with Hepatitis C Infection.

[24]   Sarin, S.K., et al. (2016) Asian-Pacific Clinical Practice Guidelines on the Management of Hepatitis B: A 2015 Update. Hepatology International, 10, 1-98.
https://doi.org/10.1007/s12072-015-9675-4

[25]   Afdhal, N., Bedossa, P., Friedrich-Rust, M., Han, K.-H. and Pinzani, M. (2015) EASL-ALEH Clinical Practice Guidelines: Non-Invasive Tests for Evaluation of Liver Disease Severity and Prognosis. Journal of Hepatology, 63, 237-264.
https://doi.org/10.1016/j.jhep.2015.04.006

[26]   AASLD and IDSA (2014) Recommendations for Testing, Managing and Treating Hepatitis C. Aasld, 1-51.

[27]   Schwenzer, N.F., Springer, F., Schraml, C., Stefan, N., Machann, J. and Schick, F. (2009) Non-Invasive Assessment and Quantification of Liver Steatosis by Ultrasound, Computed Tomography and Magnetic Resonance. Journal of Hepatology, 51, 433-445.
https://doi.org/10.1016/j.jhep.2009.05.023

[28]   Calès, P., et al. (2008) Reproducibility of Blood Tests of Liver Fibrosis in Clinical Practice. Clinical Biochemistry, 41, 10-18.
https://doi.org/10.1016/j.clinbiochem.2007.08.009

[29]   Angulo, P., et al. (2007) The NAFLD Fibrosis Score: A Noninvasive System that Identifies Liver Fibrosis in Patients with NAFLD. Hepatology, 45, 846-854.
https://doi.org/10.1002/hep.21496

[30]   Vallet-Pichard, A., et al. (2007) FIB-4: An Inexpensive and Accurate Marker of Fibrosis in HCV Infection Comparison with Liver Biopsy and Fibrotest. Hepatology, 46, 32-36.
https://doi.org/10.1002/hep.21669

[31]   Clark, J.M. (2006) The Epidemiology of Nonalcoholic Fatty Liver Disease in Adults. Journal of Clinical Gastroenterology, 40, S5-S10.

[32]   Fatima, M. and Pasha, M. (2017) Survey of Machine Learning Algorithms for Disease Diagnostic. Journal of Intelligent Learning Systems and Applications, 9, 1-16.
https://doi.org/10.4236/jilsa.2017.91001

[33]   Vijayarani, D.S. and Dhayanand, M.S. (2020) Liver Disease Prediction Using SVM and Naive Bayes Algorithms.

[34]   Hadizadeh, F., Faghihimani, E. and Adibi, P. (2017) Nonalcoholic Fatty Liver Disease: Diagnostic Biomarkers. World Journal of Gastrointestinal Pathophysiology, 8, 11-26.
https://doi.org/10.4291/wjgp.v8.i2.11

[35]   Neuman, M.G., Cohen, L.B. and Nanau, R.M. (2014) Biomarkers in Nonalcoholic Fatty Liver Disease. Canadian Journal of Gastroenterology and Hepatology, 28, 607-618.
https://doi.org/10.1155/2014/757929

[36]   Gaudette, L. and Japkowicz, N. (2009) Evaluation Methods for Ordinal Classification. In: Advances in Artificial Intelligence, Springer, Berlin, Heidelberg, 207-210.
https://doi.org/10.1007/978-3-642-01818-3_25

[37]   Munteanu, M., Ratziu, V., Morra, R., Messous, D., Imbert-Bismut, F. and Poynard, T. (2008) Noninvasive Biomarkers for the Screening of Fibrosis, Steatosis and Steatohepatitis in Patients with Metabolic Risk Factors: Fibro Test-Fibro Max Experience. Journal of Gastrointestinal and Liver Diseases, 17, 187-191.

[38]   Marchesini, G., Roden, M. and Vettor, R. (2017) Response to: Comment to EASL-EASD-EASO Clinical Practice Guidelines for the Management of Non-Alcoholic Fatty Liver Disease. Journal of Hepatology, 66, 466-467.
https://doi.org/10.1016/j.jhep.2016.11.002

[39]   Tapper, E.B., Hunink, M.G.M., Afdhal, N.H., Lai, M. and Sengupta, N. (2016) Cost-Effectiveness Analysis: Risk Stratification of Nonalcoholic Fatty Liver Disease (NAFLD) by the Primary Care Physician Using the NAFLD Fibrosis Score. PLoS ONE, 11, e0147237.
https://doi.org/10.1371/journal.pone.0147237

[40]   ASCL.Net-Keras: The Python Deep Learning Library.
https://ascl.net/1806.022

 
 
Top