中文English
ISSN 1001-5256 (Print)
ISSN 2097-3497 (Online)
CN 22-1108/R
Volume 38 Issue 1
Jan.  2022
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Article Contents

Application of artificial intelligence in liver transplantation

DOI: 10.3969/j.issn.1001-5256.2022.01.005
Research funding:

The Major State Research Development Program during the 13rd Five-Year Plan Period (2017ZX10203205-006-001);

National Key R & D Program of China (2017YFA0104304);

National Natural Science Foundation of China (82103448);

National Natural Science Foundation of China (81770648);

National Natural Science Foundation of China (81972286);

Guangdong Basic and Applied Basic Research Foundation (2019A1515110654);

Natural Science Foundation of Guangdong Province (2015A030312013);

Science and Technology Program of Guangdong Province (2017B020209004);

Science and Technology Program of Guangdong Province (20169013);

Science and Technology Program of Guangdong Province (2020B1212060019);

Science and Technology Program of Guangzhou (201508020262)

  • Received Date: 2021-10-11
  • Accepted Date: 2021-10-11
  • Published Date: 2022-01-20
  • With the advent of the era of 5G and big data, complex medical data with multiple dimensions and a large sample size bring both opportunities and challenges for clinical medicine in the new era. Compared with conventional methods, artificial intelligence can detect the hidden patterns within large datasets, and more and more scholars are applying such advanced technology in the diagnosis and treatment of diseases. After development and perfection for more than half a century, liver transplantation has become the most effective treatment method for end-stage liver diseases. Unlike the analysis of "single-patient" data in other fields, liver transplantation usually requires the consideration of the features of both the donor and the recipient and the variables during transplantation, thus generating a larger volume of medical data than other diseases, which is particularly in line with the advantages of artificial intelligence. Effective application of artificial intelligence and its combination with clinical research will usher in the new era of precision medicine. The advantages and limitations of artificial intelligence technology should be comprehensively discussed for the cross-application of artificial intelligence in liver transplantation, and the future directions of this field should also be proposed.

     

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  • [1]
    PASTORINO R, de VITO C, MIGLIARA G, et al. Benefits and challenges of Big Data in healthcare: An overview of the European initiatives[J]. Eur J Public Health, 2019, 29(Supplement_3): 23-27. DOI: 10.1093/eurpub/ckz168.
    [2]
    TRAN BX, VU GT, HA GH, et al. Global evolution of research in artificial intelligence in health and medicine: A bibliometric study[J]. J Clin Med, 2019, 8(3): 360. DOI: 10.3390/jcm8030360.
    [3]
    DARCY AM, LOUIE AK, ROBERTS LW. Machine learning and the profession of medicine[J]. JAMA, 2016, 315(6): 551-552. DOI: 10.1001/jama.2015.18421.
    [4]
    ESTEVA A, ROBICQUET A, RAMSUNDAR B, et al. A guide to deep learning in healthcare[J]. Nat Med, 2019, 25(1): 24-29. DOI: 10.1038/s41591-018-0316-z.
    [5]
    MCCORMACK L, PETROWSKY H, JOCHUM W, et al. Use of severely steatotic grafts in liver transplantation: A matched case-control study[J]. Ann Surg, 2007, 246(6): 940-946; discussion 946-948. DOI: 10.1097/SLA.0b013e31815c2a3f.
    [6]
    VOLK ML, RONEY M, MERION RM. Systematic bias in surgeons' predictions of the donor-specific risk of liver transplant graft failure[J]. Liver Transpl, 2013, 19(9): 987-990. DOI: 10.1002/lt.23683.
    [7]
    KUPPILI V, BISWAS M, SREEKUMAR A, et al. Extreme learning machine framework for risk stratification of fatty liver disease using ultrasound tissue characterization[J]. J Med Syst, 2017, 41(10): 152. DOI: 10.1007/s10916-017-0797-1.
    [8]
    BYRA M, STYCZYNSKI G, SZMIGIELSKI C, et al. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images[J]. Int J Comput Assist Radiol Surg, 2018, 13(12): 1895-1903. DOI: 10.1007/s11548-018-1843-2.
    [9]
    VANDERBECK S, BOCKHORST J, KOMOROWSKI R, et al. Automatic classification of white regions in liver biopsies by supervised machine learning[J]. Hum Pathol, 2014, 45(4): 785-792. DOI: 10.1016/j.humpath.2013.11.011.
    [10]
    MOCCIA S, MATTOS LS, PATRINI I, et al. Computer-assisted liver graft steatosis assessment via learning-based texture analysis[J]. Int J Comput Assist Radiol Surg, 2018, 13(9): 1357-1367. DOI: 10.1007/s11548-018-1787-6.
    [11]
    CESARETTI M, BRUSTIA R, GOUMARD C, et al. Use of artificial intelligence as an innovative method for liver graft macrosteatosis assessment[J]. Liver Transpl, 2020, 26(10): 1224-1232. DOI: 10.1002/lt.25801.
    [12]
    CROOME KP, MAROTTA P, WALL WJ, et al. Should a lower quality organ go to the least sick patient? Model for end-stage liver disease score and donor risk index as predictors of early allograft dysfunction[J]. Transplant Proc, 2012, 44(5): 1303-1306. DOI: 10.1016/j.transproceed.2012.01.115.
    [13]
    BRICEÑO J, CRUZ-RAMÍREZ 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]. J Hepatol, 2014, 61(5): 1020-1028. DOI: 10.1016/j.jhep.2014.05.039.
    [14]
    BRICEÑO J, AYLLÓN MD, CIRIA R. Machine-learning algorithms for predicting results in liver transplantation: The problem of donor-recipient matching[J]. Curr Opin Organ Transplant, 2020, 25(4): 406-411. DOI: 10.1097/MOT.0000000000000781.
    [16]
    DORADO-MORENO M, PÉREZ-ORTIZ M, GUTIÉRREZ PA, et al. Dynamically weighted evolutionary ordinal neural network for solving an imbalanced liver transplantation problem[J]. Artif Intell Med, 2017, 77: 1-11. DOI: 10.1016/j.artmed.2017.02.004.
    [17]
    CRUZ-RAMÍREZ M, HERVÁS-MARTÍNEZ C, FERNÁNDEZ JC, et al. Predicting patient survival after liver transplantation using evolutionary multi-objective artificial neural networks[J]. Artif Intell Med, 2013, 58(1): 37-49. DOI: 10.1016/j.artmed.2013.02.004.
    [18]
    BERTSIMAS D, KUNG J, TRICHAKIS N, et al. Development and validation of an optimized prediction of mortality for candidates awaiting liver transplantation[J]. Am J Transplant, 2019, 19(4): 1109-1118. DOI: 10.1111/ajt.15172.
    [19]
    GUIJO-RUBIO D, BRICEÑO J, GUTIÉRREZ PA, et al. Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation[J]. PLoS One, 2021, 16(5): e0252068. DOI: 10.1371/journal.pone.0252068.
    [20]
    VAGEFI PA, BERTSIMAS D, HIROSE R, et al. The rise and fall of the model for end-stage liver disease score and the need for an optimized machine learning approach for liver allocation[J]. Curr Opin Organ Transplant, 2020, 25(2): 122-125. DOI: 10.1097/MOT.0000000000000734.
    [21]
    WINGFIELD LR, CERESA C, THOROGOOD S, et al. Using artificial intelligence for predicting survival of individual grafts in liver transplantation: A systematic review[J]. Liver Transpl, 2020, 26(7): 922-934. DOI: 10.1002/lt.25772.
    [22]
    LIU CL, SOONG RS, LEE WC, et al. Predicting short-term survival after liver transplantation using machine learning[J]. Sci Rep, 2020, 10(1): 5654. DOI: 10.1038/s41598-020-62387-z.
    [23]
    CHEN C, YANG D, GAO S, et al. Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation[J]. Respir Res, 2021, 22(1): 94. DOI: 10.1186/s12931-021-01690-3.
    [24]
    HOOT N, ARONSKY D. Using Bayesian networks to predict survival of liver transplant patients[J]. AMIA Annu Symp Proc, 2005, 2005: 345-349.
    [25]
    KANTIDAKIS G, PUTTER H, LANCIA C, et al. Survival prediction models since liver transplantation-comparisons between Cox models and machine learning techniques[J]. BMC Med Res Methodol, 2020, 20(1): 277. DOI: 10.1186/s12874-020-01153-1.
    [26]
    ZHANG M, YIN F, CHEN B, et al. Mortality risk after liver transplantation in hepatocellular carcinoma recipients: A nonlinear predictive model[J]. Surgery, 2012, 151(6): 889-897. DOI: 10.1016/j.surg.2011.12.034.
    [28]
    LAU L, KANKANIGE Y, RUBINSTEIN B, et al. Machine-learning algorithms predict graft failure after liver transplantation[J]. Transplantation, 2017, 101(4): e125-e132. DOI: 10.1097/TP.0000000000001600.
    [29]
    BHAT V, TAZARI M, WATT KD, et al. New-onset diabetes and preexisting diabetes are associated with comparable reduction in long-term survival after liver transplant: A machine learning approach[J]. Mayo Clin Proc, 2018, 93(12): 1794-1802. DOI: 10.1016/j.mayocp.2018.06.020.
    [30]
    ANDRES A, MONTANO-LOZA A, GREINER R, et al. A novel learning algorithm to predict individual survival after liver transplantation for primary sclerosing cholangitis[J]. PLoS One, 2018, 13(3): e0193523. DOI: 10.1371/journal.pone.0193523.
    [31]
    WADHWANI SI, HSU EK, SHAFFER ML, et al. Predicting ideal outcome after pediatric liver transplantation: An exploratory study using machine learning analyses to leverage studies of pediatric liver transplantation data[J]. Pediatr Transplant, 2019, 23(7): e13554. DOI: 10.1111/petr.13554.
    [32]
    YASODHARA A, DONG V, AZHIE A, et al. Identifying modifiable predictors of long-term survival in liver transplant recipients with diabetes mellitus using machine learning[J]. Liver Transpl, 2021, 27(4): 536-547. DOI: 10.1002/lt.25930.
    [33]
    JAIN V, BANSAL A, RADAKOVICH N, et al. Machine learning models to predict major adverse cardiovascular events after orthotopic liver transplantation: A cohort study[J]. J Cardiothorac Vasc Anesth, 2021, 35(7): 2063-2069. DOI: 10.1053/j.jvca.2021.02.006.
    [34]
    ZHANG Y, YANG D, LIU Z, et al. An explainable supervised machine learning predictor of acute kidney injury after adult deceased donor liver transplantation[J]. J Transl Med, 2021, 19(1): 321. DOI: 10.1186/s12967-021-02990-4.
    [35]
    JIANG YQ, CAO SE, CAO S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. J Cancer Res Clin Oncol, 2021, 147(3): 821-833. DOI: 10.1007/s00432-020-03366-9.
    [36]
    AKBILGIC O, DAVIS RL. The promise of machine learning: When will it be delivered?[J]. J Card Fail, 2019, 25(6): 484-485. DOI: 10.1016/j.cardfail.2019.04.006.
    [37]
    CARUANA R, LOU Y, GEHRKE J, et al. Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission[C]. ACM, 2015.
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