Vittal A, Ghany MG. WHO guidelines for prevention, care and treatment of individuals infected with HBV a US perspective. Clin Liver Dis. 2019;23(3):417–432

Article  Google Scholar 

Lampertico P, Agarwal K, Berg T, et al. EASL 2017 clinical practice guidelines on the management of hepatitis B virus infection. J Hepatol. 2017;67(2):370–398

Article  Google Scholar 

Terrault NA, Lok ASF, McMahon BJ, et al. Update on prevention, diagnosis, and treatment of chronic hepatitis B: AASLD 2018 hepatitis B guidance. Hepatology. 2018;67(4):1560–1599

Article  Google Scholar 

Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. The METAVIR cooperative study group. Hepatology. 1996;24(2):289–293

CAS  Article  Google Scholar 

Tan M, Bhadoria AS, Cui F, et al. Estimating the proportion of people with chronic hepatitis B virus infection eligible for hepatitis B antiviral treatment worldwide: a systematic review and meta-analysis. Lancet Gastroenterol. 2021;6(2):106–119

Google Scholar 

Zheng R-Q, Wang Q-H, Lu M-D, et al. Liver fibrosis in chronic viral hepatitis: an ultrasonographic study. World J Gastroenterol. 2003;9(11):2484

Article  Google Scholar 

Colli A, Fraquelli M, Andreoletti M, et al. Severe liver fibrosis or cirrhosis: accuracy of US for detection—analysis of 300 cases. Radiology. 2003;227(1):89–94

Article  Google Scholar 

Salvatore V, Borghi A, Peri E, et al. Relationship between hepatic haemodynamics assessed by Doppler ultrasound and liver stiffness. Dig Liver Dis. 2012;44(2):154–159

Article  Google Scholar 

Wai CT, Greenson JK, Fontana RJ, et al. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518–526

Article  Google Scholar 

Sterling RK, Lissen E, Clumeck N, et al. Development of a simple noninvasive index to predict significant fibrosis in patients with HIV/HCV coinfection. Hepatology. 2006;43(6):1317–1325

CAS  Article  Google Scholar 

Siddiqui MS, Yamada G, Vuppalanchi R, et al. Diagnostic accuracy of noninvasive fibrosis models to detect change in fibrosis stage. Clin Gastroenterol Hepatol. 2019;17(9):1877–1885

Article  Google Scholar 

Conti F, Serra C, Vukotic R, et al. Assessment of liver fibrosis with elastography point quantification vs other noninvasive methods. Clin Gastroenterol Hepatol. 2019;17(3):510–517

Article  Google Scholar 

Kakegawa T, Sugimoto K, Kuroda H, et al. Diagnostic accuracy of two-dimensional shear wave elastography for liver fibrosis: a multicenter prospective study. Clin Gastroenterol Hepatol. 2021. https://doi.org/10.1016/j.cgh.2021.08.021

Article  PubMed  Google Scholar 

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(4):729–741

CAS  Article  Google Scholar 

Lee JH, Joo I, Kang TW, et al. Deep learning with ultrasonography: automated classification of liver fibrosis using a deep convolutional neural network. Eur Radiol. 2020;30(2):1264–1273

Article  Google Scholar 

Ruan D, Shi Y, Jin L, et al. An ultrasound image-based deep multi-scale texture network for liver fibrosis grading in patients with chronic HBV infection. Liver Int. 2021. https://doi.org/10.1111/liv.14999

Article  PubMed  Google Scholar 

Petitclerc L, Sebastiani G, Gilbert G, et al. Liver fibrosis: review of current imaging and MRI quantification techniques. J Magn Reson Imaging. 2017;45(5):1276–1295

Article  Google Scholar 

Hui AY, Chan HL, Wong VW, et al. Identification of chronic hepatitis B patients without significant liver fibrosis by a simple noninvasive predictive model. Am J Gastroenterol. 2005;100(3):616–623

Article  Google Scholar 

Zeng MD, Lu LG, Mao YM, et al. Prediction of significant fibrosis in HBeAg-positive patients with chronic hepatitis B by a noninvasive model. Hepatology. 2005;42(6):1437–1445

Article  Google Scholar 

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2016; pp. 770-778

Clopper CJ, Pearson ES. The use of confidence or fiducial limits illustrated in the case of the binomial. Biometrika. 1934;26(4):404–413

Article  Google Scholar 

Delong ER, Delong DM, Clarkepearson DI. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837–845

CAS  Article  Google Scholar 

Lee YA, Wallace MC, Friedman SL. Pathobiology of liver fibrosis: a translational success story. Gut. 2015;64(5):830–841

CAS  Article  Google Scholar 

Xiao G, Yang J, Yan L. Comparison of diagnostic accuracy of aspartate aminotransferase to platelet ratio index and fibrosis-4 index for detecting liver fibrosis in adult patients with chronic hepatitis B virus infection: a systemic review and meta-analysis. Hepatology. 2015;61(1):292–302

Article  Google Scholar 

Patel K, Sebastiani G. Limitations of non-invasive tests for assessment of liver fibrosis. JHEP Rep. 2020;2(2):100067

Article  Google Scholar 

Martinez SM, Crespo G, Navasa M, et al. Noninvasive assessment of liver fibrosis. Hepatology. 2011;53(1):325–335

Article  Google Scholar 

Ferraioli G, Wong VW-S, Castera L, et al. Liver ultrasound elastography: an update to the world federation for ultrasound in medicine and biology guidelines and recommendations. Ultrasound Med Biol. 2018;44(12):2419–2440

Article  Google Scholar 

Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: Int Conf Learn Represent; 2015; San Diego, CA, US.

Szegedy C, Vanhoucke V, Ioffe S, et al. Rethinking the inception architecture for computer vision. In: IEEE Conf Comput Vision Pattern Recognit; 2016; Las Vegas, NV, USA.

Huang G, Liu Z, Van der Maaten L, et al. Densely Connected Convolutional Networks. In: IEEE Conf Comput Vision Pattern Recognit; 2017; Honolulu, HI, USA.