1. Yip TC, Vilar-Gomez E, Petta S, Yilmaz Y, Wong GL, Adams LA, et al. Geographical similarity and differences in the burden and genetic predisposition of NAFLD. Hepatology 2022 Sep 5;doi:
10.1002/hep.32774.
3. Ekstedt M, Hagström H, Nasr P, Fredrikson M, Stål P, Kechagias S, et al. Fibrosis stage is the strongest predictor for diseasespecific mortality in NAFLD after up to 33 years of follow-up. Hepatology 2015;61:1547-1554.
4. Le P, Payne JY, Zhang L, Deshpande A, Rothberg MB, Alkhouri N, et al. Disease state transition probabilities across the spectrum of NAFLD: a systematic review and meta-analysis of paired biopsy or imaging studies. Clin Gastroenterol Hepatol 2022 Aug 4;doi:
10.1016/j.cgh.2022.07.033.
5. Wong VW, Chitturi S, Wong GL, Yu J, Chan HL, Farrell GC. Pathogenesis and novel treatment options for non-alcoholic steatohepatitis. Lancet Gastroenterol Hepatol 2016;1:56-67.
7. Davison BA, Harrison SA, Cotter G, Alkhouri N, Sanyal A, Edwards C, et al. Suboptimal reliability of liver biopsy evaluation has implications for randomized clinical trials. J Hepatol 2020;73:1322-1332.
9. Kleiner DE, Brunt EM, Van Natta M, Behling C, Contos MJ, Cummings OW, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology 2005;41:1313-1321.
11. Bedossa P, Poitou C, Veyrie N, Bouillot JL, Basdevant A, Paradis V, et al. Histopathological algorithm and scoring system for evaluation of liver lesions in morbidly obese patients. Hepatology 2012;56:1751-1759.
12. Bedossa P; FLIP Pathology Consortium. Utility and appropriateness of the fatty liver inhibition of progression (FLIP) algorithm and steatosis, activity, and fibrosis (SAF) score in the evaluation of biopsies of nonalcoholic fatty liver disease. Hepatology 2014;60:565-575.
13. Younossi ZM, Ratziu V, Loomba R, Rinella M, Anstee QM, Goodman Z, et al. Obeticholic acid for the treatment of nonalcoholic steatohepatitis: interim analysis from a multicentre, randomised, placebo-controlled phase 3 trial. Lancet 2019;394:2184-2196.
18. Shen J, Chan HL, Wong GL, Choi PC, Chan AW, Chan HY, et al. Non-invasive diagnosis of non-alcoholic steatohepatitis by combined serum biomarkers. J Hepatol 2012;56:1363-1370.
22. Ferraioli G, Berzigotti A, Barr RG, Choi BI, Cui XW, Dong Y, et al. Quantification of liver fat content with ultrasound: a WFUMB position paper. Ultrasound Med Biol 2021;47:2803-2820.
23. European Association for the Study of the Liver (EASL); European Association for the Study of Diabetes (EASD); European Association for the Study of Obesity (EASO). EASL-EASD-EASO clinical practice guidelines for the management of non-alcoholic fatty liver disease. J Hepatol 2016;64:1388-1402.
24. Hernaez R, Lazo M, Bonekamp S, Kamel I, Brancati FL, Guallar E, et al. Diagnostic accuracy and reliability of ultrasonography for the detection of fatty liver: a meta-analysis. Hepatology 2011;54:1082-1090.
25. Charatcharoenwitthaya P, Lindor KD. Role of radiologic modalities in the management of non-alcoholic steatohepatitis. Clin Liver Dis 2007;11:37-54 viii.
27. Hamaguchi M, Kojima T, Itoh Y, Harano Y, Fujii K, Nakajima T, et al. The severity of ultrasonographic findings in nonalcoholic fatty liver disease reflects the metabolic syndrome and visceral fat accumulation. Am J Gastroenterol 2007;102:2708-2715.
28. Ballestri S, Lonardo A, Romagnoli D, Carulli L, Losi L, Day CP, et al. Ultrasonographic fatty liver indicator, a novel score which rules out NASH and is correlated with metabolic parameters in NAFLD. Liver Int 2012;32:1242-1252.
29. Wong VW, Vergniol J, Wong GL, Foucher J, Chan HL, Le Bail B, et al. Diagnosis of fibrosis and cirrhosis using liver stiffness measurement in nonalcoholic fatty liver disease. Hepatology 2010;51:454-462.
32. Alkhouri N, Herring R, Kabler H, Kayali Z, Hassanein T, Kohli A, et al. Safety and efficacy of combination therapy with semaglutide, cilofexor and firsocostat in patients with non-alcoholic steatohepatitis: a randomised, open-label phase II trial. J Hepatol 2022;77:607-618.
33. Younossi ZM, Anstee QM, Wai-Sun Wong V, Trauner M, Lawitz EJ, Harrison SA, et al. The association of histologic and noninvasive tests with adverse clinical and patient-reported outcomes in patients with advanced fibrosis due to nonalcoholic steatohepatitis. Gastroenterology 2021;160:1608-1619.e13.
36. Cardoso AC, Tovo CV, Leite NC, El Bacha IA, Calçado FL, Coral GP, et al. Validation and performance of FibroScan
®-AST (FAST) score on a Brazilian population with nonalcoholic fatty liver disease. Dig Dis Sci 2022;67:5272-5279.
41. Andersson A, Kelly M, Imajo K, Nakajima A, Fallowfield JA, Hirschfield G, et al. Clinical utility of magnetic resonance imaging biomarkers for identifying nonalcoholic steatohepatitis patients at high risk of progression: a multicenter pooled data and meta-analysis. Clin Gastroenterol Hepatol 2022;20:2451-2461.e3.
43. Jayakumar S, Middleton MS, Lawitz EJ, Mantry PS, Caldwell SH, Arnold H, et al. Longitudinal correlations between MRE, MRIPDFF, and liver histology in patients with non-alcoholic steatohepatitis: analysis of data from a phase II trial of selonsertib. J Hepatol 2019;70:133-141.
44. Tamaki N, Munaganuru N, Jung J, Yonan AQ, Loomba RR, Bettencourt R, et al. Clinical utility of 30% relative decline in MRIPDFF in predicting fibrosis regression in non-alcoholic fatty liver disease. Gut 2022;71:983-990.
45. Jung J, Loomba RR, Imajo K, Madamba E, Gandhi S, Bettencourt R, et al. MRE combined with FIB-4 (MEFIB) index in detection of candidates for pharmacological treatment of NASH-related fibrosis. Gut 2021;70:1946-1953.
47. Kim BK, Tamaki N, Imajo K, Yoneda M, Sutter N, Jung J, et al. Head-to-head comparison between MEFIB, MAST, and FAST for detecting stage 2 fibrosis or higher among patients with NAFLD. J Hepatol 2022;77:1482-1490.
48. Ajmera V, Kim BK, Yang K, Majzoub AM, Nayfeh T, Tamaki N, et al. Liver stiffness on magnetic resonance elastography and the MEFIB index and liver-related outcomes in nonalcoholic fatty liver disease: a systematic review and meta-analysis of individual participants. Gastroenterology 2022;163:1079-1089.e5.
49. Noureddin M, Truong E, Gornbein JA, Saouaf R, Guindi M, Todo T, et al. MRI-based (MAST) score accurately identifies patients with NASH and significant fibrosis. J Hepatol 2022;76:781-787.
52. Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, et al. Application of artificial intelligence to gastroenterology and hepatology. Gastroenterology 2020;158:76-94.e2.
53. Fialoke S, Malarstig A, Miller MR, Dumitriu A. Application of machine learning methods to predict non-alcoholic steatohepatitis (NASH) in non-alcoholic fatty liver (NAFL) patients. AMIA Annu Symp Proc 2018;2018:430-439.
56. Perakakis N, Polyzos SA, Yazdani A, Sala-Vila A, Kountouras J, Anastasilakis AD, et al. Non-invasive diagnosis of nonalcoholic steatohepatitis and fibrosis with the use of omics and supervised learning: a proof of concept study. Metabolism 2019;101:154005.
62. Jana A, Qu H, Rattan P, Minacapelli CD, Rustgi V, Metaxas D. Deep Learning based NAS Score and Fibrosis Stage Prediction from CT and Pathology Data. 2020 Ieee 20th International Conference on Bioinformatics and Bioengineering (Bibe 2020) 2020;981-986.
64. Jaume G, Pati P, Bozorgtabar B, et al. Quantifying Explainers of Graph Neural Networks in Computational Pathology. 2021 Ieee/ Cvf Conference on Computer Vision and Pattern Recognition, Cvpr 2021;2021:8102-8112.
65. Dwivedi C, Nofallah S, Pouryahya M, et al. Multi stain graph fusion for multimodal integration in pathology. 2021 Ieee/Cvf Conference on Computer Vision and Pattern Recognition, Cvpr 2021;2021:1835-1845.
66. Tan Q, Ye M, Ma AJ, Yang B, Yip TC, Wong GL, et al. Explainable uncertainty-aware convolutional recurrent neural network for irregular medical time series. IEEE Trans Neural Netw Learn Syst 2021;32:4665-4679.
67. Tan Q, Ye M, Lai-Hung Wong G, Yuen PC. Cooperative joint attentive network for patient outcome prediction on irregular multi-rate multivariate health data. Zhou Z-H. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence: International Joint Conferences on Artificial Intelligence Organization. 2021. p. 1586-1592.
68. Suresha PB, Wang Y, Xiao C, Glass L, Yuan Y, Clifford GD. A deep learning approach for classifying nonalcoholic steatohepatitis patients from nonalcoholic fatty liver disease patients using electronic medical records. Shaban-Nejad A, Michalowski M, Buckeridge DL. Explainable AI in Healthcare and Medicine. Cham: Springer International Publishing. 2021. p. 107-1113.
69. Yin C, Liu S, Wong VW-S, Yuen PC. Learning Sparse Interpretable Features For NAS Scoring From Liver Biopsy Images. Raedt LD. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence: International Joint Conferences on Artificial Intelligence Organization. 2022. p. 1580-1586.
70. Yin C, Liu S, Shao R, Yuen PC. Focusing on clinically interpretable features: selective attention regularization for liver biopsy image classification. de Bruijne M, Cattin PC, Cotin S, Padoy N, Speidel S, Zheng Y, et al. Medical Image Computing and Computer Assisted Intervention - MICCAI 2021. Cham: Springer International Publishing; 2021. p. 153-162.
71. Lyu F, Ma AJ, Yip TC, Wong GL, Yuen PC. Weakly supervised liver tumor segmentation using couinaud segment annotation. IEEE Trans Med Imaging 2022;41:1138-1149.