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The use of neural networks (NNs) as a cutting-edge technique in the medical field has drawn considerable attention. NN models "learn" from a large amount of data and then find corresponding clinical patterns that are challenging for clinicians to recognize. In this study, we focus on liver transplantation (LT), which is an effective treatment for end-stage liver diseases. The management before and after LT produces a massive quantity of medical data, which can be fully processed by NNs. We describe recent progress in the clinical application of NNs to LT in five respects: pre-transplantation evaluation of the donor and recipient, recipient outcome prediction, allocation system development, operation monitoring, and post-transplantation complication prediction. This review provides clinicians and researchers with a description of forefront applications of NNs in the field of LT and discusses prospects and pitfalls.
The use of neural networks (NNs) as a cutting-edge technique in the medical field has drawn considerable attention. NN models "learn" from a large amount of data and then find corresponding clinical patterns that are challenging for clinicians to recognize. In this study, we focus on liver transplantation (LT), which is an effective treatment for end-stage liver diseases. The management before and after LT produces a massive quantity of medical data, which can be fully processed by NNs. We describe recent progress in the clinical application of NNs to LT in five respects: pre-transplantation evaluation of the donor and recipient, recipient outcome prediction, allocation system development, operation monitoring, and post-transplantation complication prediction. This review provides clinicians and researchers with a description of forefront applications of NNs in the field of LT and discusses prospects and pitfalls.
Olivera P, Danese S, Jay N, et al. Big data in IBD: a look into the future. Nat Rev Gastroenterol Hepatol 2019;16: 312–21. https://doi.org/10.1038/s41575-019-0102-5.
Park SH, Mazumder NR, Mehrotra S, et al. Artificial intelligence-related literature in transplantation: a practical guide. Transplantation 2021;105: 704–8. https://doi.org/10.1097/TP.0000000000003304.
Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med 2019; 380: 1347–58. https://doi.org/10.1056/nejmra1814259.
Edwards AS, Kaplan B, Jie T. A primer on machine learning. Transplantation 2020; 105: 699–703. https://doi.org/10.1097/tp.0000000000003316.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521: 436–44. https://doi.org/10.1038/nature14539.
Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36: 569–80. https://doi.org/10.1111/jgh.15415.
Ferrarese A, Sartori G, Orrù G, et al. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int 2021;34: 398–411. https://doi.org/10.1111/tri.13818.
Spann A, Yasodhara A, Kang J, et al. Applying machine learning in liver disease and transplantation: a comprehensive review. Hepatology 2020;71: 1093–105. https://doi.org/10.1002/hep.31103.
Wingfield LR, Ceresa C, Thorogood S, et al. Using artificial intelligence for predicting survival of individual grafts in liver transplantation: a systematic review. Liver Transplant 2020;26: 922–34. https://doi.org/10.1002/lt.25772.
Tran J, Sharma D, Gotlieb N, et al. Application of machine learning in liver transplantation: a review. Hepatol Int 2022;16: 495–508. https://doi.org/10.1007/s12072-021-10291-7.
Abiodun OI, et al. State-of-the-art in artificial neural network applications: a survey. Heliyon 2018;4: e00938. https://doi.org/10.1016/j.heliyon.2018.e00938.
Sun L, Marsh JN, Matlock MK, et al. Deep learning quantification of percent steatosis in donor liver biopsy frozen sections. EBioMedicine 2020;60. https://doi.org/10.1016/j.ebiom.2020.103029.103029-103029.
Kavur AE, Gezer NS, Barış M, et al. Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors. Diagn Interventional Radiol 2020;26: 11–21. https://doi.org/10.5152/dir.2019.19025.
Allard MA, Baillié e G, Castro-Benitez C, et al. Prediction of the total liver weight using anthropological clinical parameters: does complexity result in better accuracy? HPB 2017;19: 338–44. https://doi.org/10.1016/j.hpb.2016.11.012.
Salvi M, Molinaro L, Metovic J, et al. Fully automated quantitative assessment of hepatic steatosis in liver transplants. Comput Biol Med 2020;123: 103836. https://doi.org/10.1016/j.compbiomed.2020.103836.
Lim J, Han S, Lee D, et al. Identification of hepatic steatosis in living liver donors by machine learning models. Hepatol Commun 2022;6: 1689–98. https://doi.org/10.1002/hep4.1921.
Tiukinhoy-Laing SD, Rossi JS, Bayram M, et al. Cardiac hemodynamic and coronary angiographic characteristics of patients being evaluated for liver transplantation. Am J Cardiol 2006;98: 178–81. https://doi.org/10.1016/j.amjcard.2006.01.089.
Schuessler M, Saner F, Al-Rashid F, et al. Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm. Eur Radiol 2022. https://doi.org/10.1007/s00330-022-08921-1.
Dutkowski P, Linecker M, DeOliveira ML, et al. Challenges to liver transplantation and strategies to improve outcomes. Gastroenterology 2015;148: 307–23. https://doi.org/10.1053/j.gastro.2014.08.045.
Rana A, Hardy HA, Halazun KJ, et al. Survival outcomes following liver transplantation (SOFT) score: a novel method to predict patient survival following liver transplantation. Am J Transplant 2008;8: 2537–46. https://doi.org/10.1111/j.1600-6143.2008.02400.x.
Dutkowski P, Oberkofler CE, Slankamenac K, et al. Are there better guidelines for allocation in liver transplantation? A novel score targeting justice and utility in the model for end-stage liver disease era. Ann Surg 2011;254: 745–53. https://doi.org/10.1097/SLA.0b013e3182365081.discussion753.
Ershoff BD, Lee CK, Wray CL, et al. Training and validation of deep neural networks for the prediction of 90-day post-liver transplant mortality using UNOS registry data. Transplant Proc 2020;52: 246–58. https://doi.org/10.1016/j.transproceed.2019.10.019.
Lau L, Kankanige Y, Rubinstein B, et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation 2017;101: e125–32. https://doi.org/10.1097/tp.0000000000001600.
Molinari M, Ayloo S, Tsung A, et al. Prediction of perioperative mortality of cadaveric liver transplant recipients during their evaluations. Transplantation 2019; 103: e297–307. https://doi.org/10.1097/TP.0000000000002810.
Kong L, Lv T, Jiang L, et al. A simple four-factor preoperative recipient scoring model for prediction of 90-day mortality after adult liver transplantation: A retrospective cohort study. Int J Surg 2020;81: 26–31. https://doi.org/10.1016/j.ijsu.2020.07.021.
Yang M, Peng B, Zhuang Q, et al. Models to predict the short-term survival of acuteon-chronic liver failure patients following liver transplantation. BMC Gastroenterol 2022;22: 80. https://doi.org/10.1186/s12876-022-02164-6.
Kantidakis G, Putter H, Lancia C, et al. Survival prediction models since liver transplantation – comparisons between cox models and machine learning techniques. BMC Med Res Methodol 2020;20:277. https://doi.org/10.1186/s12874-020-01153-1.
Nam JY, Lee J-H, Bae J, et al. Novel model to predict HCC recurrence after liver transplantation obtained using deep learning: a multicenter study. Cancers 2020;12: 2791. https://doi.org/10.3390/cancers12102791.
Ivanics T, Nelson W, Patel MS, et al. The Toronto postliver transplantation hepatocellular carcinoma recurrence calculator: a machine learning approach. Liver Transplant 2022;28:593–602. https://doi.org/10.1002/lt.26332.
Ayllón MD, Ciria R, Cruz-Ramírez M, et al. Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation. Liver Transplant 2018;24:192–203. https://doi.org/10.1002/lt.24870.
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 Hepatol 2014;61:1020–8. https://doi.org/10.1016/j.jhep.2014.05.039.
Guijo-Rubio D, Briceño J, Gutiérrez PA, et al. Statistical methods versus machine learning techniques for donor-recipient matching in liver transplantation. PLoS One 2021;16:e0252068. https://doi.org/10.1371/journal.pone.0252068.
Buhre W, Rossaint R. Perioperative management and monitoring in anaesthesia. Lancet 2003;362:1839–46. https://doi.org/10.1016/s0140-6736(03)14905-7.
Park M, Han S, Kim GS, et al. Evaluation of new calibrated pulse-wave analysis (VolumeViewTM/EV1000TM) for cardiac output monitoring undergoing living donor liver transplantation. PLoS One 2016;11:e0164521. https://doi.org/10.1371/journal.pone.0164521.
Moon Y-J, Moon HS, Kim D-S, et al. Deep learning-based stroke volume estimation outperforms conventional arterial contour method in patients with hemodynamic instability. J Clin Med 2019;8:1419. https://doi.org/10.3390/jcm8091419.
Liu LP, Zhao QY, Wu J, et al. Machine learning for the prediction of red blood cell transfusion in patients during or after liver transplantation surgery. Front Med 2021;8:632210. https://doi.org/10.3389/fmed.2021.632210.
Chen S, Liu LP, Wang YJ, et al. Advancing prediction of risk of intraoperative massive blood transfusion in liver transplantation with machine learning models. A multicenter retrospective study. Front Neuroinf 2022;16:893452. https://doi.org/10.3389/fninf.2022.893452.
He Z-L, Zhou J-B, Liu Z-K, et al. Application of machine learning models for predicting acute kidney injury following donation after cardiac death liver transplantation. Hepatobiliary Pancreat Dis Int 2021;20:222–31. https://doi.org/10.1016/j.hbpd.2021.02.001.
Lee H-C, Yoon SB, Yang S-M, et al. Prediction of acute kidney injury after liver transplantation: machine learning approaches vs. logistic regression model. J Clin Med 2018;7:428. https://doi.org/10.3390/jcm7110428.
Cooper JP, Perkins JD, Warner PR, et al. Acute graft-versus-host disease after orthotopic liver transplantation: predicting this rare complication using machine learning. Liver Transplant 2022;28:407–21. https://doi.org/10.1002/lt.26318.
Nitski O, Azhie A, Qazi-Arisar FA, et al. Long-term mortality risk stratification of liver transplant recipients: real-time application of deep learning algorithms on longitudinal data. Lancet Digit Health 2021;3:e295–305. https://doi.org/10.1016/s2589-7500(21)00040-6.
Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism 2017;69: S36–40. https://doi.org/10.1016/j.metabol.2017.01.011.
Wang P, Berzin TM, Glissen Brown JR, et al. Real-time automatic detection system increases colonoscopic polyp and adenoma detection rates: a prospective randomised controlled study. Gut 2019;68:1813–9. https://doi.org/10.1136/gutjnl-2018-317500.
Fan FL, Xiong J, Li M, et al. On interpretability of artificial neural networks: a survey. IEEE Trans Radiat Plasma Med Sci 2021;5:741–60. https://doi.org/10.1109/trpms.2021.3066428.
van der Velden BHM, Kuijf HJ, Gilhuijs KGA, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal 2022;79: 102470. https://doi.org/10.1016/j.media.2022.102470.
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell 2019;1:206–15. https://doi.org/10.1038/s42256-019-0048-x.
Chen P-HC, Liu Y, Peng L. How to develop machine learning models for healthcare. Nat Mater 2019;18:410–4. https://doi.org/10.1038/s41563-019-0345-0.
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