Artificial Intelligence in Liver Diseases: Recent Advances.
Artificial intelligence
Cirrhosis
Deep learning
Fibrosis
Hepatic
Liver disease
Machine learning
Journal
Advances in therapy
ISSN: 1865-8652
Titre abrégé: Adv Ther
Pays: United States
ID NLM: 8611864
Informations de publication
Date de publication:
29 Jan 2024
29 Jan 2024
Historique:
received:
12
09
2023
accepted:
03
01
2024
medline:
30
1
2024
pubmed:
30
1
2024
entrez:
29
1
2024
Statut:
aheadofprint
Résumé
Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.
Identifiants
pubmed: 38286960
doi: 10.1007/s12325-024-02781-5
pii: 10.1007/s12325-024-02781-5
doi:
Types de publication
Journal Article
Review
Langues
eng
Informations de copyright
© 2024. The Author(s), under exclusive licence to Springer Healthcare Ltd., part of Springer Nature.
Références
Asrani SK, Devarbhavi H, Eaton J, Kamath PS. Burden of liver diseases in the world. J Hepatol. 2019;70(1):151–71.
pubmed: 30266282
doi: 10.1016/j.jhep.2018.09.014
Ray G. Management of liver diseases: current perspectives. World J Gastroenterol. 2022;28(40):5818–26.
pubmed: 36353204
pmcid: 9639658
doi: 10.3748/wjg.v28.i40.5818
Jiménez Pérez M, Grande RG. Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: a review. World J Gastroenterol. 2020;26(37):5617–28.
pubmed: 33088156
pmcid: 7545389
doi: 10.3748/wjg.v26.i37.5617
Cao JS, Lu ZY, Chen MY, et al. Artificial intelligence in gastroenterology and hepatology: status and challenges. World J Gastroenterol. 2021;27(16):1664–90.
pubmed: 33967550
pmcid: 8072192
doi: 10.3748/wjg.v27.i16.1664
Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol. 2021;36(3):569–80.
pubmed: 33709606
doi: 10.1111/jgh.15415
Lai Q, Spoletini G, Mennini G, et al. Prognostic role of artificial intelligence among patients with hepatocellular cancer: a systematic review. World J Gastroenterol. 2020;26(42):6679–88.
pubmed: 33268955
pmcid: 7673961
doi: 10.3748/wjg.v26.i42.6679
Dinani AM, Kowdley KV, Noureddin M. Application of artificial intelligence for diagnosis and risk stratification in NAFLD and NASH: the state of the art. Hepatology. 2021;74(4):2233–40.
pubmed: 33928671
doi: 10.1002/hep.31869
Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–43.
pubmed: 29507784
pmcid: 5829945
doi: 10.1136/svn-2017-000101
Schattenberg JM, Chalasani N, Alkhouri N. Artificial intelligence applications in hepatology. Clin Gastroenterol Hepatol. 2023;21(8):2015–25.
pubmed: 37088460
doi: 10.1016/j.cgh.2023.04.007
Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of artificial intelligence for the diagnosis and treatment of liver diseases. Hepatology. 2021;73(6):2546–63.
pubmed: 33098140
doi: 10.1002/hep.31603
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. Diabetologia 2016; 59(6):1121–40.
Pouwels S, Sakran N, Graham Y, et al. Non-alcoholic fatty liver disease (NAFLD): a review of pathophysiology, clinical management and effects of weight loss. BMC Endocr Disord. 2022;22(1):63.
pubmed: 35287643
pmcid: 8919523
doi: 10.1186/s12902-022-00980-1
Méndez-Sánchez N, Bugianesi E, Gish RG, et al. Global multi-stakeholder endorsement of the MAFLD definition. Lancet Gastroenterol Hepatol. 2022;7(5):388–90.
pubmed: 35248211
doi: 10.1016/S2468-1253(22)00062-0
Nan Y, An J, Bao J, et al. The Chinese Society of Hepatology position statement on the redefinition of fatty liver disease. J Hepatol. 2021;75(2):454–61.
pubmed: 34019941
doi: 10.1016/j.jhep.2021.05.003
Mendez-Sanchez N, Arrese M, Gadano A, et al. The Latin American Association for the Study of the Liver (ALEH) position statement on the redefinition of fatty liver disease. Lancet Gastroenterol Hepatol. 2021;6(1):65–72.
pubmed: 33181118
doi: 10.1016/S2468-1253(20)30340-X
Lazarus JV, Newsome PN, Francque SM, et al. Reply: A multi-society delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023 Nov 20. https://doi.org/10.1097/HEP.0000000000000696 .
doi: 10.1097/HEP.0000000000000696
pubmed: 37984709
Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016;64(1):73–84.
pubmed: 26707365
doi: 10.1002/hep.28431
Paik JM, Golabi P, Younossi Y, Mishra A, Younossi ZM. Changes in the global burden of chronic liver diseases from 2012 to 2017: the growing impact of NAFLD. Hepatology. 2020;72(5):1605–16.
pubmed: 32043613
doi: 10.1002/hep.31173
Zhang L, Mao Y. Artificial intelligence in NAFLD: will liver biopsy still be necessary in the future? Healthcare (Basel). 2022;11(1):117.
pubmed: 36611577
pmcid: 9818843
doi: 10.3390/healthcare11010117
Zhang YN, Fowler KJ, Hamilton G, et al. Liver fat imaging-a clinical overview of ultrasound, CT, and MR imaging. Br J Radiol. 2018;91(1089):20170959.
pubmed: 29722568
pmcid: 6223150
doi: 10.1259/bjr.20170959
Zsombor Z, Rónaszéki AD, Csongrády B, et al. Evaluation of artificial intelligence-calculated hepatorenal index for diagnosing mild and moderate hepatic steatosis in non-alcoholic fatty liver disease. Medicina (Kaunas). 2023;59(3):469.
pubmed: 36984470
doi: 10.3390/medicina59030469
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(5):554–69.
pubmed: 33594747
doi: 10.1111/hepr.13628
Sorino P, Campanella A, Bonfiglio C, et al. Development and validation of a neural network for NAFLD diagnosis. Sci Rep. 2021;11(1):20240.
pubmed: 34642390
pmcid: 8511336
doi: 10.1038/s41598-021-99400-y
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(5):409–15.
pubmed: 34420885
doi: 10.1016/j.hbpd.2021.08.004
Cao W, An X, Cong L, Lyu C, Zhou Q, Guo R. Application of deep learning in quantitative analysis of 2-dimensional ultrasound imaging of nonalcoholic fatty liver disease. J Ultrasound Med. 2020;39(1):51–9.
pubmed: 31222786
doi: 10.1002/jum.15070
Van Vleck TT, Chan L, Coca SG, et al. Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression. Int J Med Informatics. 2019;129:334–41.
doi: 10.1016/j.ijmedinf.2019.06.028
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.
pubmed: 31711876
doi: 10.1016/j.metabol.2019.154005
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(12):934–43.
pubmed: 30631398
pmcid: 6323515
doi: 10.4254/wjh.v10.i12.934
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(12):1895–903.
pubmed: 30094778
pmcid: 6223753
doi: 10.1007/s11548-018-1843-2
Perveen S, Shahbaz M, Keshavjee K, Guergachi A. A systematic machine learning based approach for the diagnosis of non-alcoholic fatty liver disease risk and progression. Sci Rep. 2018;8(1):2112.
pubmed: 29391513
pmcid: 5794753
doi: 10.1038/s41598-018-20166-x
Ma H, Xu CF, Shen Z, Yu CH, Li YM. 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.
pubmed: 30402478
pmcid: 6192080
doi: 10.1155/2018/4304376
Yip TC, Ma AJ, Wong VW, et al. Laboratory parameter-based machine learning model for excluding non-alcoholic fatty liver disease (NAFLD) in the general population. Aliment Pharmacol Ther. 2017;46(4):447–56.
pubmed: 28585725
doi: 10.1111/apt.14172
Redman JS, Natarajan Y, Hou JK, et al. Accurate identification of fatty liver disease in data warehouse utilizing natural language processing. Dig Dis Sci. 2017;62(10):2713–8.
pubmed: 28861720
doi: 10.1007/s10620-017-4721-9
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(10):152.
pubmed: 28836045
doi: 10.1007/s10916-017-0797-1
Corey KE, Kartoun U, Zheng H, Shaw SY. Development and validation of an algorithm to identify nonalcoholic fatty liver disease in the electronic medical record. Dig Dis Sci. 2016;61(3):913–9.
pubmed: 26537487
doi: 10.1007/s10620-015-3952-x
Vanderbeck S, Bockhorst J, Komorowski R, Kleiner DE, Gawrieh S. Automatic classification of white regions in liver biopsies by supervised machine learning. Hum Pathol. 2014;45(4):785–92.
pubmed: 24565203
doi: 10.1016/j.humpath.2013.11.011
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 Ann Symp. 2018;2018:430–9.
Goldshtein I, Chodick G, Kochba I, Gal N, Webb M, Shibolet O. Identification and characterization of nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2020;18(8):1887–9.
pubmed: 31404663
doi: 10.1016/j.cgh.2019.08.007
Schneider CV, Li T, Zhang D, et al. Large-scale identification of undiagnosed hepatic steatosis using natural language processing. EClinicalMedicine. 2023;62:102149.
pubmed: 37599905
pmcid: 10432816
doi: 10.1016/j.eclinm.2023.102149
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(2):342–50.
pubmed: 32096706
doi: 10.1148/radiol.2020191160
Huo Y, Terry JG, Wang J, et al. Fully automatic liver attenuation estimation combing CNN segmentation and morphological operations. Med Phys. 2019;46(8):3508–19.
pubmed: 31228267
pmcid: 6692233
doi: 10.1002/mp.13675
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(5):767–75.
pubmed: 25776030
pmcid: 8320703
doi: 10.1016/j.humpath.2015.01.019
Qu H, Minacapelli CD, Tait C, et al. Training of computational algorithms to predict NAFLD activity score and fibrosis stage from liver histopathology slides. Comput Methods Programs Biomed. 2021;207:106153.
pubmed: 34020377
doi: 10.1016/j.cmpb.2021.106153
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(6):1235–41.
pubmed: 33684933
pmcid: 8200272
doi: 10.1093/jamia/ocab003
Iredale JP. Models of liver fibrosis: exploring the dynamic nature of inflammation and repair in a solid organ. J Clin Invest. 2007;117(3):539–48.
pubmed: 17332881
pmcid: 1804370
doi: 10.1172/JCI30542
Ji GW, Zhu FP, Xu Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: a multi-institutional study. EBioMedicine. 2019;50:156–65.
pubmed: 31735556
pmcid: 6923482
doi: 10.1016/j.ebiom.2019.10.057
Wu L, Ning B, Yang J, Chen Y, Zhang C, Yan Y. Diagnosis of liver cirrhosis and liver fibrosis by artificial intelligence algorithm-based multislice spiral computed tomography. Comput Math Methods Med. 2022;2022:1217003.
pubmed: 35341007
pmcid: 8941514
Shan L, Liu Z, Ci L, Shuai C, Lv X, Li J. Research progress on the anti-hepatic fibrosis action and mechanism of natural products. Int Immunopharmacol. 2019;75:105765.
pubmed: 31336335
doi: 10.1016/j.intimp.2019.105765
Qiu QT, Zhang J, Duan JH, Wu SZ, Ding JL, Yin Y. Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis. Chin Med J (Engl). 2020;133(22):2653–9.
pubmed: 33009025
doi: 10.1097/CM9.0000000000001113
Manka P, Zeller A, Syn WK. Fibrosis in chronic liver disease: an update on diagnostic and treatment modalities. Drugs. 2019;79(9):903–27.
pubmed: 31119644
doi: 10.1007/s40265-019-01126-9
Lai M, Afdhal NH. Liver fibrosis determination. Gastroenterol Clin North Am. 2019;48(2):281–9.
pubmed: 31046975
doi: 10.1016/j.gtc.2019.02.002
Anteby R, Klang E, Horesh N, et al. Deep learning for noninvasive liver fibrosis classification: a systematic review. Liver Int. 2021;41(10):2269–78.
pubmed: 34008300
doi: 10.1111/liv.14966
Zhang H, Luo K, Deng R, Li S, Duan S. Deep learning-based CT imaging for the diagnosis of liver tumor. Comput Intell Neurosci. 2022;2022:3045370.
pubmed: 35755728
pmcid: 9225866
Liu JQ, Ren JY, Xu XL, et al. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol. 2022;28(38):5530–46.
pubmed: 36304086
pmcid: 9594013
doi: 10.3748/wjg.v28.i38.5530
Zhou LQ, Wang JY, Yu SY, et al. Artificial intelligence in medical imaging of the liver. World J Gastroenterol. 2019;25(6):672–82.
pubmed: 30783371
pmcid: 6378542
doi: 10.3748/wjg.v25.i6.672
Ahmed Y, Hussein RS, Basha TA, et al. Detecting liver fibrosis using a machine learning-based approach to the quantification of the heart-induced deformation in tagged MR images. NMR Biomed. 2020;33(1):e4215.
pubmed: 31730265
doi: 10.1002/nbm.4215
Schawkat K, Ciritsis A, von Ulmenstein S, et al. Diagnostic accuracy of texture analysis and machine learning for quantification of liver fibrosis in MRI: correlation with MR elastography and histopathology. Eur Radiol. 2020;30(8):4675–85.
pubmed: 32270315
doi: 10.1007/s00330-020-06831-8
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):729–41.
pubmed: 29730602
doi: 10.1136/gutjnl-2018-316204
Fu TT, Yao Z, Ding H, et al. Computer-aided assessment of liver fibrosis progression in patients with chronic hepatitis B: an exploratory research. Zhonghua Yi Xue Za Zhi. 2019;99(7):491–5.
pubmed: 30786344
Li W, Huang Y, Zhuang BW, et al. Multiparametric ultrasomics of significant liver fibrosis: a machine learning-based analysis. Eur Radiol. 2019;29(3):1496–506.
pubmed: 30178143
doi: 10.1007/s00330-018-5680-z
Li N, Zhang J, Wang S, et al. Machine learning assessment for severity of liver fibrosis for chronic HBV based on physical layer with serum markers. IEEE Access. 2019;7:124351–65.
doi: 10.1109/ACCESS.2019.2923688
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Liver fibrosis: deep convolutional neural network for staging by using gadoxetic acid-enhanced hepatobiliary phase MR images. Radiology. 2018;287(1):146–55.
pubmed: 29239710
doi: 10.1148/radiol.2017171928
Yasaka K, Akai H, Kunimatsu A, Abe O, Kiryu S. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018;28(11):4578–85.
pubmed: 29761358
doi: 10.1007/s00330-018-5499-7
Choi KJ, Jang JK, Lee SS, et al. Development and validation of a deep learning system for staging liver fibrosis by using contrast agent-enhanced CT images in the liver. Radiology. 2018;289(3):688–97.
pubmed: 30179104
doi: 10.1148/radiol.2018180763
Shousha HI, Awad AH, Omran DA, Elnegouly MM, Mabrouk M. Data mining and machine learning algorithms using IL28B genotype and biochemical markers best predicted advanced liver fibrosis in chronic hepatitis C. Jpn J Infect Dis. 2018;71(1):51–7.
pubmed: 29279441
doi: 10.7883/yoken.JJID.2017.089
Wei R, Wang J, Wang X, et al. Clinical prediction of HBV and HCV related hepatic fibrosis using machine learning. EBioMedicine. 2018;35:124–32.
pubmed: 30100397
pmcid: 6154783
doi: 10.1016/j.ebiom.2018.07.041
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.
pubmed: 28779596
doi: 10.1016/j.compbiomed.2017.07.012
Zhang L, Li QY, Duan YY, Yan GZ, Yang YL, Yang RJ. Artificial neural network aided non-invasive grading evaluation of hepatic fibrosis by duplex ultrasonography. BMC Med Inform Decis Mak. 2012;12:55.
pubmed: 22716936
pmcid: 3444307
doi: 10.1186/1472-6947-12-55
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.
pubmed: 20735842
pmcid: 2939639
doi: 10.1186/1471-2334-10-251
Obeid JS, Khalifa A, Xavier B, Bou-Daher H, Rockey DC. An AI approach for identifying patients with cirrhosis. J Clin Gastroenterol. 2023;57(1):82–8.
pubmed: 34238846
pmcid: 8741865
doi: 10.1097/MCG.0000000000001586
Duan YY, Qin J, Qiu WQ, et al. Performance of a generative adversarial network using ultrasound images to stage liver fibrosis and predict cirrhosis based on a deep-learning radiomics nomogram. Clin Radiol. 2022;77(10):e723–31.
pubmed: 35811157
doi: 10.1016/j.crad.2022.06.003
Luetkens JA, Nowak S, Mesropyan N, et al. Deep learning supports the differentiation of alcoholic and other-than-alcoholic cirrhosis based on MRI. Sci Rep. 2022;12(1):8297.
pubmed: 35585118
pmcid: 9117223
doi: 10.1038/s41598-022-12410-2
Nowak S, Mesropyan N, Faron A, et al. Detection of liver cirrhosis in standard T2-weighted MRI using deep transfer learning. Eur Radiol. 2021;31(11):8807–15.
pubmed: 33974149
pmcid: 8523404
doi: 10.1007/s00330-021-07858-1
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):1264–73.
pubmed: 31478087
doi: 10.1007/s00330-019-06407-1
Procopet B, Cristea VM, Robic MA, et al. Serum tests, liver stiffness and artificial neural networks for diagnosing cirrhosis and portal hypertension. Dig Liver Dis. 2015;47(5):411–6.
pubmed: 25732434
doi: 10.1016/j.dld.2015.02.001
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.
pubmed: 25678995
pmcid: 4313459
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(1):121–6.
pubmed: 20703578
doi: 10.1007/s10916-009-9348-8
Agbim U, Asrani SK. Non-invasive assessment of liver fibrosis and prognosis: an update on serum and elastography markers. Expert Rev Gastroenterol Hepatol. 2019;13(4):361–74.
pubmed: 30791772
doi: 10.1080/17474124.2019.1579641
Hu C, Anjur V, Saboo K, et al. Low predictability of readmissions and death using machine learning in cirrhosis. Am J Gastroenterol. 2021;116(2):336–46.
pubmed: 33038139
doi: 10.14309/ajg.0000000000000971
Simsek C, Sahin H, Emir Tekin I, Koray Sahin T, Yasemin Balaban H, Sivri B. Artificial intelligence to predict overall survivals of patients with cirrhosis and outcomes of variceal bleeding. Hepatol Forum. 2021;2(2):55–9.
pubmed: 35783899
pmcid: 9138923
Zou WY, Enchakalody BE, Zhang P, et al. Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosis. Hepatol Commun. 2021;5(11):1901–10.
pubmed: 34558818
pmcid: 8557320
doi: 10.1002/hep4.1768
Nitsch J, Sack J, Halle MW, et al. MRI-based radiomic feature analysis of end-stage liver disease for severity stratification. Int J Comput Assist Radiol Surg. 2021;16(3):457–66.
pubmed: 33646521
pmcid: 7946682
doi: 10.1007/s11548-020-02295-9
Kanwal F, Taylor TJ, Kramer JR, et al. Development, validation, and evaluation of a simple machine learning model to predict cirrhosis mortality. JAMA Netw Open. 2020;3(11):e2023780.
pubmed: 33141161
pmcid: 7610191
doi: 10.1001/jamanetworkopen.2020.23780
de Franchis R, Bosch J, Garcia-Tsao G, Reiberger T, Ripoll C. Baveno VII—renewing consensus in portal hypertension. J Hepatol. 2022;76(4):959–74.
pubmed: 35120736
doi: 10.1016/j.jhep.2021.12.022
Yu Q, Huang Y, Li X, et al. An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension. Cell Rep Med. 2022;3(3):100563.
pubmed: 35492878
pmcid: 9040173
doi: 10.1016/j.xcrm.2022.100563
Chakraborty E, Sarkar D. Emerging therapies for hepatocellular carcinoma (HCC). Cancers. 2022;14(11):2798.
pubmed: 35681776
pmcid: 9179883
doi: 10.3390/cancers14112798
Yang JD, Heimbach JK. New advances in the diagnosis and management of hepatocellular carcinoma. BMJ. 2020;371:m3544.
pubmed: 33106289
doi: 10.1136/bmj.m3544
Lurje I, Czigany Z, Bednarsch J, et al. Treatment strategies for hepatocellular carcinoma—a multidisciplinary approach. Int J Mol Sci. 2019;20(6):1465.
pubmed: 30909504
pmcid: 6470895
doi: 10.3390/ijms20061465
Xu X, Mao Y, Tang Y, et al. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on radiomic analysis. Comput Math Methods Med. 2022;2022:5334095.
pubmed: 35237341
pmcid: 8885247
Gao R, Zhao S, Aishanjiang K, et al. Deep learning for differential diagnosis of malignant hepatic tumors based on multi-phase contrast-enhanced CT and clinical data. J Hematol Oncol. 2021;14(1):154.
pubmed: 34565412
pmcid: 8474892
doi: 10.1186/s13045-021-01167-2
Kim DW, Lee G, Kim SY, et al. Deep learning-based algorithm to detect primary hepatic malignancy in multiphase CT of patients at high risk for HCC. Eur Radiol. 2021;31(9):7047–57.
pubmed: 33738600
doi: 10.1007/s00330-021-07803-2
Stollmayer R, Budai BK, Tóth A, et al. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging. World J Gastroenterol. 2021;27(35):5978–88.
pubmed: 34629814
pmcid: 8475009
doi: 10.3748/wjg.v27.i35.5978
Zhou J, Wang W, Lei B, et al. Automatic detection and classification of focal liver lesions based on deep convolutional neural networks: a preliminary study. Front Oncol. 2020;10:581210.
pubmed: 33585197
doi: 10.3389/fonc.2020.581210
Huang Q, Pan F, Li W, et al. Differential diagnosis of atypical hepatocellular carcinoma in contrast-enhanced ultrasound using spatio-temporal diagnostic semantics. IEEE J Biomed Health Inform. 2020;24(10):2860–9.
pubmed: 32149699
doi: 10.1109/JBHI.2020.2977937
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(3):343–54.
pubmed: 29630528
doi: 10.3233/CH-170275
Zheng R, Wang L, Wang C, et al. Feasibility of automatic detection of small hepatocellular carcinoma (≤2 cm) in cirrhotic liver based on pattern matching and deep learning. Phys Med Biol. 2021;66(8):085014.
doi: 10.1088/1361-6560/abf2f8
Ben-Cohen A, Klang E, Diamant I, et al. CT image-based decision support system for categorization of liver metastases into primary cancer sites: initial results. Acad Radiol. 2017;24(12):1501–9.
pubmed: 28778512
doi: 10.1016/j.acra.2017.06.008
Yang Q, Wei J, Hao X, et al. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: a multicentre study. EBioMedicine. 2020;56:102777.
pubmed: 32485640
pmcid: 7262550
doi: 10.1016/j.ebiom.2020.102777
Wang R, He Y, Yao C, et al. Classification and segmentation of hyperspectral data of hepatocellular carcinoma samples using 1-D convolutional neural network. Cytometry A. 2020;97(1):31–8.
pubmed: 31403260
doi: 10.1002/cyto.a.23871
Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PUP. LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from HandE stained liver histopathology images. Int J Comput Assist Radiol Surg. 2021;16(9):1549–63.
pubmed: 34053009
doi: 10.1007/s11548-021-02410-4
Shan QY, Hu HT, Feng ST, et al. CT-based peritumoral radiomics signatures to predict early recurrence in hepatocellular carcinoma after curative tumor resection or ablation. Cancer Imaging. 2019;19(1):11.
pubmed: 30813956
pmcid: 6391838
doi: 10.1186/s40644-019-0197-5
Liu Z, Liu Y, Zhang W, et al. Deep learning for prediction of hepatocellular carcinoma recurrence after resection or liver transplantation: a discovery and validation study. Hepatol Int. 2022;16(3):577–89.
pubmed: 35352293
doi: 10.1007/s12072-022-10321-y
He T, Fong JN, Moore LW, et al. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph. 2021;89: 101894.
pubmed: 33725579
pmcid: 8054468
doi: 10.1016/j.compmedimag.2021.101894
Rodriguez-Luna H, Vargas HE, Byrne T, Rakela J. Artificial neural network and tissue genotyping of hepatocellular carcinoma in liver-transplant recipients: prediction of recurrence. Transplantation. 2005;79(12):1737–40.
pubmed: 15973178
doi: 10.1097/01.TP.0000161794.32007.D1
Liu D, Liu F, Xie X, et al. Accurate prediction of responses to transarterial chemoembolization for patients with hepatocellular carcinoma by using artificial intelligence in contrast-enhanced ultrasound. Eur Radiol. 2020;30(4):2365–76.
pubmed: 31900703
doi: 10.1007/s00330-019-06553-6
Peng J, Kang S, Ning Z, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30(1):413–24.
pubmed: 31332558
doi: 10.1007/s00330-019-06318-1
Abajian A, Murali N, Savic LJ, et al. Predicting treatment response to intra-arterial therapies for hepatocellular carcinoma with the use of supervised machine learning-an artificial intelligence concept. J Vasc Interv Radiol. 2018;29(6):850-7.e1.
pubmed: 29548875
pmcid: 5970021
doi: 10.1016/j.jvir.2018.01.769
Kim SH, Lee JM, Kim JH, et al. Appropriateness of a donor liver with respect to macrosteatosis: application of artificial neural networks to US images–initial experience. Radiology. 2005;234(3):793–803.
pubmed: 15665225
doi: 10.1148/radiol.2343040142
Moccia S, Mattos LS, Patrini I, et al. Computer-assisted liver graft steatosis assessment via learning-based texture analysis. Int J Comput Assist Radiol Surg. 2018;13(9):1357–67.
pubmed: 29796834
doi: 10.1007/s11548-018-1787-6
Briceno J, Cruz-Ramirez M, Prieto M, et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J Hepatol. 2014;61(5):1020–8.
pubmed: 24905493
doi: 10.1016/j.jhep.2014.05.039
Bertsimas D, Kung J, Trichakis N, Wang Y, Hirose R, Vagefi PA. Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation. Am J Transplant. 2019;19(4):1109–18.
pubmed: 30411495
doi: 10.1111/ajt.15172
Bredt LC, Peres LAB, Risso M, Barros L. Risk factors and prediction of acute kidney injury after liver transplantation: logistic regression and artificial neural network approaches. World J Hepatol. 2022;14(3):570–82.
pubmed: 35582300
pmcid: 9055199
doi: 10.4254/wjh.v14.i3.570
Chen C, Yang D, Gao S, et al. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation. Respir Res. 2021;22(1):94.
pubmed: 33789673
pmcid: 8011203
doi: 10.1186/s12931-021-01690-3
He ZL, Zhou JB, Liu ZK, et al. Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation. Hepatobiliary Pancreat Dis Int. 2021;20(3):222–31.
pubmed: 33726966
doi: 10.1016/j.hbpd.2021.02.001
Kantidakis G, Putter H, Lancia C, Boer J, Braat AE, Fiocco M. Survival prediction models since liver transplantation—comparisons between Cox models and machine learning techniques. BMC Med Res Methodol. 2020;20(1):277.
pubmed: 33198650
pmcid: 7667810
doi: 10.1186/s12874-020-01153-1
Liu CL, Soong RS, Lee WC, Jiang GW, Lin YC. Predicting short-term survival after liver transplantation using machine learning. Sci Rep. 2020;10(1):5654.
pubmed: 32221367
pmcid: 7101323
doi: 10.1038/s41598-020-62387-z
Kazemi A, Kazemi K, Sami A, Sharifian R. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques. Exp Clin Transplant. 2019;17(6):775–83.
pubmed: 30968757
doi: 10.6002/ect.2018.0170
Molinari M, Ayloo S, Tsung A, et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation. 2019;103(10):e297–307.
pubmed: 31283673
doi: 10.1097/TP.0000000000002810
Wadhwani SI, Hsu EK, Shaffer ML, Anand R, Ng VL, Bucuvalas JC. Predicting ideal outcome after pediatric liver transplantation: an exploratory study using machine learning analyses to leverage studies of pediatric liver transplantation data. Pediatr Transplant. 2019;23(7):e13554.
pubmed: 31328849
doi: 10.1111/petr.13554
Lee HC, Yoon SB, Yang SM, et al. Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model. J Clin Med. 2018;7(11):428.
pubmed: 30413107
pmcid: 6262324
doi: 10.3390/jcm7110428
Khosravi B, Pourahmad S, Bahreini A, Nikeghbalian S, Mehrdad G. Five years survival of patients after liver transplantation and its effective factors by neural network and Cox poroportional hazard regression models. Hepat Mon. 2015;15(9):e25164.
pubmed: 26500682
pmcid: 4612564
doi: 10.5812/hepatmon.25164
Lau L, Kankanige Y, Rubinstein B, et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation. 2017;101(4):e125–32.
pubmed: 27941428
pmcid: 7228574
doi: 10.1097/TP.0000000000001600
Hughes VF, Melvin DG, Niranjan M, Alexander GA, Trull AK. Clinical validation of an artificial neural network trained to identify acute allograft rejection in liver transplant recipients. Liver Transpl. 2001;7(6):496–503.
pubmed: 11443576
doi: 10.1053/jlts.2001.24642
Barri YM, Sanchez EQ, Jennings LW, et al. Acute kidney injury following liver transplantation: definition and outcome. Liver Transpl. 2009;15(5):475–83.
pubmed: 19399734
doi: 10.1002/lt.21682
Li X, Wei X, Chen C, et al. N-Acetylcysteine inhalation improves pulmonary function in patients received liver transplantation. Biosci Rep. 2018 Sep 28;38(5):BSR20180858. https://doi.org/10.1042/BSR20180858 .
doi: 10.1042/BSR20180858
pubmed: 29624843
pmcid: 6097911
Andrade RJ, Chalasani N, Björnsson ES, et al. Drug-induced liver injury. Nat Rev Dis Primers. 2019;5(1):58.
pubmed: 31439850
doi: 10.1038/s41572-019-0105-0
Hoofnagle JH, Björnsson ES. Drug-induced liver injury—types and phenotypes. N Engl J Med. 2019;381(3):264–73.
pubmed: 31314970
doi: 10.1056/NEJMra1816149
Wang K, Zhang L, Li L, et al. Identification of drug-induced liver injury biomarkers from multiple microarrays based on machine learning and bioinformatics analysis. Int J Mol Sci. 2022;23(19):11945.
pubmed: 36233241
pmcid: 9570393
doi: 10.3390/ijms231911945
Yen JS, Hu CC, Huang WH, Hsu CW, Yen TH, Weng CH. An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis. Hum Exp Toxicol. 2021;40(11):1947–54.
pubmed: 33955253
doi: 10.1177/09603271211014587
Puri M. Automated machine learning diagnostic support system as a computational biomarker for detecting drug-induced liver injury patterns in whole slide liver pathology images. Assay Drug Dev Technol. 2020;18(1):1–10.
pubmed: 31149832
pmcid: 6998050
doi: 10.1089/adt.2019.919
Smith BP, Auvil LS, Welge M, et al. Identification of early liver toxicity gene biomarkers using comparative supervised machine learning. Sci Rep. 2020;10(1):19128.
pubmed: 33154507
pmcid: 7645727
doi: 10.1038/s41598-020-76129-8
Fu H, Shen Z, Lai R, et al. Clinic-radiomics model using liver magnetic resonance imaging helps predict chronicity of drug-induced liver injury. Hepatol Int. 2023;17(6):1626–36.
pubmed: 37188998
doi: 10.1007/s12072-023-10539-4