Volume 14 Issue 4
Jul.  2023
Turn off MathJax
Article Contents
Ren Xiangge, Zhai Wensheng, Li Bing. Research progress on application of artificial intelligence in the field of kidney transplantation[J]. ORGAN TRANSPLANTATION, 2023, 14(4): 505-513. doi: 10.3969/j.issn.1674-7445.2023.04.006
Citation: Ren Xiangge, Zhai Wensheng, Li Bing. Research progress on application of artificial intelligence in the field of kidney transplantation[J]. ORGAN TRANSPLANTATION, 2023, 14(4): 505-513. doi: 10.3969/j.issn.1674-7445.2023.04.006

Research progress on application of artificial intelligence in the field of kidney transplantation

doi: 10.3969/j.issn.1674-7445.2023.04.006
More Information
  • Corresponding author: Zhai Wensheng, Email: zhws65415@sina.com
  • Received Date: 2023-03-23
    Available Online: 2023-07-13
  • Publish Date: 2023-07-15
  • In recent years, artificial intelligence has been persistently developed and increasingly applied in the medical field, including risk prediction, diagnosis and treatment of various diseases, which enhances the diagnosis and management levels of diseases and shows a promising application prospect in the medical field. Artificial intelligence has been rapidly advanced in the field of kidney transplantation. Researchers have attempted to apply it in multiple scenarios, such as preoperative evaluation and prediction of postoperative complications of kidney transplantation, prompting that artificial intelligence has tremendous application prospect in the field of kidney transplantation. In this article, the application of artificial intelligence in donor-recipient matching, evaluation of renal allograft function, prediction of clinical outcomes, diagnosis of postoperative complications, monitoring and management of immunosuppressants were reviewed, research progress on the application of artificial intelligence in the field of kidney transplantation was summarized, and the limitations of artificial intelligence were discussed, aiming to provide reference for promoting the practical application and popularization of artificial intelligence in the field of kidney transplantation.

     

  • loading
  • [1]
    JIANG F, JIANG Y, ZHI H, et al. Artificial intelligence in healthcare: past, present and future[J]. Stroke Vasc Neurol, 2017, 2(4): 230-243. DOI: 10.1136/svn-2017-000101.
    [2]
    邵琨, 王祥慧. 2020年肾移植临床国际前沿热点及新进展荟萃[J]. 器官移植, 2021, 12(2): 155-168. DOI: 10.3969/j.issn.1674-7445.2021.02.005.

    SHAO K, WANG XH. Highlights of international frontier hot spots and new progress on renal transplantation in 2020[J]. Organ Transplant, 2021, 12(2): 155-168. DOI: 10.3969/j.issn.1674-7445.2021.02.005.
    [3]
    LENTINE KL, SMITH JM, HART A, et al. OPTN/SRTR 2020 annual data report: kidney[J]. Am J Transplant, 2022, 22(Suppl 2): 21-136. DOI: 10.1111/ajt.16982.
    [4]
    SEYAHI N, OZCAN SG. Artificial intelligence and kidney transplantation[J]. World J Transplant, 2021, 11(7): 277-289. DOI: 10.5500/wjt.v11.i7.277.
    [5]
    COOREY CP, SHARMA A, MULLER S, et al. Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes[J]. Kidney Int, 2021, 99(4): 817-823. DOI: 10.1016/j.kint.2020.08.026.
    [6]
    TADDEO M, FLORIDI L. How AI can be a force for good[J]. Science, 2018, 361(6404): 751-752. DOI: 10.1126/science.aat5991.
    [7]
    GREENER JG, KANDATHIL SM, MOFFAT L, et al. A guide to machine learning for biologists[J]. Nat Rev Mol Cell Biol, 2022, 23(1): 40-55. DOI: 10.1038/s41580-021-00407-0.
    [8]
    LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
    [9]
    CHOI RY, COYNER AS, KALPATHY-CRAMER J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9(2): 14. DOI: 10.1167/tvst.9.2.14.
    [10]
    BINI SA. Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?[J]. J Arthroplasty, 2018, 33(8): 2358-2361. DOI: 10.1016/j.arth.2018.02.067.
    [11]
    RAZZAQ M, CLÉMENT F, YVINEC R. An overview of deep learning applications in precocious puberty and thyroid dysfunction[J]. Front Endocrinol (Lausanne), 2022, 13: 959546. DOI: 10.3389/fendo.2022.959546.
    [12]
    PLACONA AM, MARTINEZ C, MCGEHEE H, et al. Can donor narratives yield insights? a natural language processing proof of concept to facilitate kidney allocation[J]. Am J Transplant, 2020, 20(4): 1095-1104. DOI: 10.1111/ajt.15705.
    [13]
    HAMOUDA E, EL-METWALLY S, TAREK M. Ant lion optimization algorithm for kidney exchanges[J]. PLoS One, 2018, 13(5): e0196707. DOI: 10.1371/journal.pone.0196707.
    [14]
    RASHIDI KHAZAEE P, BAGHERZADEH J, NIAZKHANI Z, et al. A dynamic model for predicting graft function in kidney recipients' upcoming follow up visits: a clinical application of artificial neural network[J]. Int J Med Inform, 2018, 119: 125-133. DOI: 10.1016/j.ijmedinf.2018.09.012.
    [15]
    VAN LOON E, ZHANG W, COEMANS M, et al. Forecasting of patient-specific kidney transplant function with a sequence-to-sequence deep learning model[J]. JAMA Netw Open, 2021, 4(12): e2141617. DOI: 10.1001/jamanetworkopen.2021.41617.
    [16]
    IMPROTA G, MAZZELLA V, VECCHIONE D, et al. Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-transplant patients[J]. J Eval Clin Pract, 2020, 26(4): 1224-1234. DOI: 10.1111/jep.13302.
    [17]
    ACS B, RANTALAINEN M, HARTMAN J. Artificial intelligence as the next step towards precision pathology[J]. J Intern Med, 2020, 288(1): 62-81. DOI: 10.1111/joim.13030.
    [18]
    XIE Q, FAUST K, VAN OMMEREN R, et al. Deep learning for image analysis: personalizing medicine closer to the point of care[J]. Crit Rev Clin Lab Sci, 2019, 56(1): 61-73. DOI: 10.1080/10408363.2018.1536111.
    [19]
    MARSH JN, MATLOCK MK, KUDOSE S, et al. Deep learning global glomerulosclerosis in transplant kidney frozen sections[J]. IEEE Trans Med Imaging, 2018, 37(12): 2718-2728. DOI: 10.1109/TMI.2018.2851150.
    [20]
    HERMSEN M, DE BEL T, DEN BOER M, et al. Deep learning-based histopathologic assessment of kidney tissue[J]. J Am Soc Nephrol, 2019, 30(10): 1968-1979. DOI: 10.1681/ASN.2019020144.
    [21]
    SALVI M, MOGETTA A, GAMBELLA A, et al. Automated assessment of glomerulosclerosis and tubular atrophy using deep learning[J]. Comput Med Imaging Graph, 2021, 90: 101930. DOI: 10.1016/j.compmedimag.2021.101930.
    [22]
    YI Z, SALEM F, MENON MC, et al. Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies[J]. Kidney Int, 2022, 101(2): 288-298. DOI: 10.1016/j.kint.2021.09.028.
    [23]
    FARRIS AB, VIZCARRA J, AMGAD M, et al. Image analysis pipeline for renal allograft evaluation and fibrosis quantification[J]. Kidney Int Rep, 2021, 6(7): 1878-1887. DOI: 10.1016/j.ekir.2021.04.019.
    [24]
    BROWN TS, ELSTER EA, STEVENS K, et al. Bayesian modeling of pretransplant variables accurately predicts kidney graft survival[J]. Am J Nephrol, 2012, 36(6): 561-569. DOI: 10.1159/000345552.
    [25]
    YOO KD, NOH J, LEE H, et al. A machine learning approach using survival statistics to predict graft survival in kidney transplant recipients: a multicenter cohort study[J]. Sci Rep, 2017, 7(1): 8904. DOI: 10.1038/s41598-017-08008-8.
    [26]
    BAE S, MASSIE AB, THOMAS AG, et al. Who can tolerate a marginal kidney? predicting survival after deceased donor kidney transplant by donor-recipient combination[J]. Am J Transplant, 2019, 19(2): 425-433. DOI: 10.1111/ajt.14978.
    [27]
    RAYNAUD M, AUBERT O, DIVARD G, et al. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study[J]. Lancet Digit Health, 2021, 3(12): e795-e805. DOI: 10.1016/S2589-7500(21)00209-0.
    [28]
    NAQVI SAA, TENNANKORE K, VINSON A, et al. Predicting kidney graft survival using machine learning methods: prediction model development and feature significance analysis study[J]. J Med Internet Res, 2021, 23(8): e26843. DOI: 10.2196/26843.
    [29]
    梁诚, 牛纪平, 满江位, 等. 管周毛细血管损伤在肾移植中作用的研究进展[J]. 器官移植, 2023, 14(1): 147-153. DOI: 10.3969/j.issn.1674-7445.2023.01.020.

    LIANG C, NIU JP, MAN JW, et al. Progress in the role of peritubular capillary injury in kidney transplantation[J]. Organ Transplant, 2023, 14(1): 147-153. DOI: 10.3969/j.issn.1674-7445.2023.01.020.
    [30]
    KIM YG, CHOI G, GO H, et al. A fully automated system using a convolutional neural network to predict renal allograft rejection: extra-validation with giga-pixel immunostained slides[J]. Sci Rep, 2019, 9(1): 5123. DOI: 10.1038/s41598-019-41479-5.
    [31]
    ABDELTAWAB H, SHEHATA M, SHALABY A, et al. A novel CNN-based CAD system for early assessment of transplanted kidney dysfunction[J]. Sci Rep, 2019, 9(1): 5948. DOI: 10.1038/s41598-019-42431-3.
    [32]
    SHEHATA M, SHALABY A, SWITALA AE, et al. A multimodal computer-aided diagnostic system for precise identification of renal allograft rejection: preliminary results[J]. Med Phys, 2020, 47(6): 2427-2440. DOI: 10.1002/mp.14109.
    [33]
    FU Q, AGARWAL D, DENG K, et al. An unbiased machine learning exploration reveals gene sets predictive of allograft tolerance after kidney transplantation[J]. Front Immunol, 2021, 12: 695806. DOI: 10.3389/fimmu.2021.695806.
    [34]
    WANG Y, ZHANG D, HU X. A three-gene peripheral blood potential diagnosis signature for acute rejection in renal transplantation[J]. Front Mol Biosci, 2021, 8: 661661. DOI: 10.3389/fmolb.2021.661661.
    [35]
    PONTRELLI P, SIMONE S, RASCIO F, et al. Pre-transplant expression of CCR-2 in kidney transplant recipients is associated with the development of delayed graft function[J]. Front Immunol, 2022, 13: 804762. DOI: 10.3389/fimmu.2022.804762.
    [36]
    KONIECZNY A, STOJANOWSKI J, RYDZYŃSKA K, et al. Artificial intelligence-a tool for risk assessment of delayed-graft function in kidney transplant[J]. J Clin Med, 2021, 10(22): 5244. DOI: 10.3390/jcm10225244.
    [37]
    JEN KY, ALBAHRA S, YEN F, et al. Automated en masse machine learning model generation shows comparable performance as classic regression models for predicting delayed graft function in renal allografts[J]. Transplantation, 2021, 105(12): 2646-2654. DOI: 10.1097/TP.0000000000003640.
    [38]
    COSTA SD, DE ANDRADE LGM, BARROSO FVC, et al. The impact of deceased donor maintenance on delayed kidney allograft function: a machine learning analysis[J]. PLoS One, 2020, 15(2): e0228597. DOI: 10.1371/journal.pone.0228597.
    [39]
    KAWAKITA S, BEAUMONT JL, JUCAUD V, et al. Personalized prediction of delayed graft function for recipients of deceased donor kidney transplants with machine learning[J]. Sci Rep, 2020, 10(1): 18409. DOI: 10.1038/s41598-020-75473-z.
    [40]
    PENG B, GONG H, TIAN H, et al. The study of the association between immune monitoring and pneumonia in kidney transplant recipients through machine learning models[J]. J Transl Med, 2020, 18(1): 370. DOI: 10.1186/s12967-020-02542-2.
    [41]
    LUO Y, TANG Z, HU X, et al. Machine learning for the prediction of severe pneumonia during posttransplant hospitalization in recipients of a deceased-donor kidney transplant[J]. Ann Transl Med, 2020, 8(4): 82. DOI: 10.21037/atm.2020.01.09.
    [42]
    XIA M, YANG H, TONG X, et al. Risk factors for new-onset diabetes mellitus after kidney transplantation: a systematic review and meta-analysis[J]. J Diabetes Investig, 2021, 12(1): 109-122. DOI: 10.1111/jdi.13317.
    [43]
    KIM JE, PARK SJ, KIM YC, et al. Deep learning-based quantification of visceral fat volumes predicts posttransplant diabetes mellitus in kidney transplant recipients[J]. Front Med (Lausanne), 2021, 8: 632097. DOI: 10.3389/fmed.2021.632097.
    [44]
    AL-IMAM A, AL-TABBAKH ALI. Predictors of new-onset diabetes after kidney transplantation during 2019-nCoV pandemic: a unison of frequentist inference and narrow AI[J]. Open Access Maced J Med Sci, 2022, 10(B): 180-191. DOI: 10.3889/oamjms.2022.7521.
    [45]
    詹世鹏, 马攀, 刘芳. 机器学习在治疗药物监测与个体化用药中的应用[J]. 中国药房, 2023, 34(1): 117-121, 128. DOI: 10.6039/j.issn.1001-0408.2023.01.23.

    ZHAN SP, MA P, LIU F. Application of machine learning in the therapeutic drug monitoring and individual drug therapy[J]. China Pharm, 2023, 34(1): 117-121, 128. DOI: 10.6039/j.issn.1001-0408.2023.01.23.
    [46]
    MAO J, CHEN Y, XU L, et al. Applying machine learning to the pharmacokinetic modeling of cyclosporine in adult renal transplant recipients: a multi-method comparison[J]. Front Pharmacol, 2022, 13: 1016399. DOI: 10.3389/fphar.2022.1016399.
    [47]
    WOILLARD JB, LABRIFFE M, DEBORD J, et al. Tacrolimus exposure prediction using machine learning[J]. Clin Pharmacol Ther, 2021, 110(2): 361-369. DOI: 10.1002/cpt.2123.
    [48]
    LABRIFFE M, WOILLARD JB, DEBORD J, et al. Machine learning algorithms to estimate everolimus exposure trained on simulated and patient pharmacokinetic profiles[J]. CPT Pharmacometrics Syst Pharmacol, 2022, 11(8): 1018-1028. DOI: 10.1002/psp4.12810.
    [49]
    彭怀东, 杨灿邦, 张磊, 等. 多重用药的肾移植受者用药现状与药学服务需求调查[J]. 中国医院药学杂志, 2022, 42(12): 1276-1281, 1292. DOI: 10.13286/j.1001-5213.2022.12.19.

    PENG HD, YANG CB, ZHANG L, et al. A questionnaire for prescription pattern and pharmaceutical care needs on renal transplant recipients with polypharmacy[J]. Chin J Hosp Pharm, 2022, 42(12): 1276-1281, 1292. DOI: 10.13286/j.1001-5213.2022.12.19.
    [50]
    MCGILLICUDDY J, CHANDLER J, SOX L, et al. "Smartphone medication adherence saves kidneys" for kidney transplantation recipients: protocol for a randomized controlled trial[J]. JMIR Res Protoc, 2019, 8(6): e13351. DOI: 10.2196/13351.
    [51]
    FLEMING JN, GEBREGZIABHER M, POSADAS A, et al. Impact of a pharmacist-led, mHealth-based intervention on tacrolimus trough variability in kidney transplant recipients: a report from the TRANSAFE Rx randomized controlled trial[J]. Am J Health Syst Pharm, 2021, 78(14): 1287-1293. DOI: 10.1093/ajhp/zxab157.
    [52]
    HAN A, MIN SI, AHN S, et al. Mobile medication manager application to improve adherence with immunosuppressive therapy in renal transplant recipients: a randomized controlled trial[J]. PLoS One, 2019, 14(11): e0224595. DOI: 10.1371/journal.pone.0224595.
    [53]
    MAGRABI F, AMMENWERTH E, MCNAIR JB, et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications[J]. Yearb Med Inform, 2019, 28(1): 128-134. DOI: 10.1055/s-0039-1677903.
    [54]
    MAHADEVAIAH G, RV P, BERMEJO I, et al. Artificial intelligence-based clinical decision support in modern medical physics: selection, acceptance, commissioning, and quality assurance[J]. Med Phys, 2020, 47(5): e228-e235. DOI: 10.1002/mp.13562.
    [55]
    GIANFRANCESCO MA, TAMANG S, YAZDANY J, et al. Potential biases in machine learning algorithms using electronic health record data[J]. JAMA Intern Med, 2018, 178(11): 1544-1547. DOI: 10.1001/jamainternmed.2018.3763.
    [56]
    KELLY CJ, KARTHIKESALINGAM A, SULEYMAN M, et al. Key challenges for delivering clinical impact with artificial intelligence[J]. BMC Med, 2019, 17(1): 195. DOI: 10.1186/s12916-019-1426-2.
    [57]
    BRIGANTI G, LE MOINE O. Artificial intelligence in medicine: today and tomorrow[J]. Front Med (Lausanne), 2020, 7: 27. DOI: 10.3389/fmed.2020.00027.
    [58]
    张姝艳, 皮婷婷. 医疗领域中人工智能应用的可解释性困境与治理[J]. 医学与哲学, 2023, 44(3): 25-29, 35. DOI: 10.12014/j.issn.1002-0772.2023.03.06.

    ZHANG SY, PI TT. Interpretability dilemma and governance of artificial intelligence application in medical field[J]. Med Philos, 2023, 44(3): 25-29, 35. DOI: 10.12014/j.issn.1002-0772.2023.03.06.
    [59]
    王涵, 陈敏. 医疗人工智能政策法律分析及对策研究[J]. 中国数字医学, 2021, 16(12): 73-77. DOI: 10.3969/j.issn.1673-7571.2021.12.017.

    WANG H, CHEN M. Policy and law analysis and countermeasures research of medical artificial intelligence[J]. China Digit Med, 2021, 16(12): 73-77. DOI: 10.3969/j.issn.1673-7571.2021.12.017.
    [60]
    郑志峰. 诊疗人工智能的医疗损害责任[J]. 中国法学, 2023(1): 203-221. DOI: 10.14111/j.cnki.zgfx.2023.01.011.

    ZHENG ZF. Liability for medical damage caused by diagnostic and therapeutic artificial intelligence[J]. China Legal Sci, 2023(1): 203-221. DOI: 10.14111/j.cnki.zgfx.2023.01.011.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(2)

    Article Metrics

    Article views (340) PDF downloads(72) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return