The Transformative Potential of Artificial Intelligence in Solid Organ Transplantation

Overview

Journal Front Transplant

Specialty General Surgery

Date 2024 Jul 12

PMID 38993779

Authors

Mouhamad Al Moussawy

Zoe S Lakkis

Zuhayr A Ansari

Aravind R Cherukuri

Khodor I Abou-Daya

Affiliations

Soon will be listed here.

Abstract

Solid organ transplantation confronts numerous challenges ranging from donor organ shortage to post-transplant complications. Here, we provide an overview of the latest attempts to address some of these challenges using artificial intelligence (AI). We delve into the application of machine learning in pretransplant evaluation, predicting transplant rejection, and post-operative patient outcomes. By providing a comprehensive overview of AI's current impact, this review aims to inform clinicians, researchers, and policy-makers about the transformative power of AI in enhancing solid organ transplantation and facilitating personalized medicine in transplant care.

Citing Articles

Trends in Precision Medicine and Pharmacogenetics as an Adjuvant in Establishing a Correct Immunosuppressive Therapy for Kidney Transplant: An Up-to-Date Historical Overview.

Belardi R, Pacifici F, Baldetti M, Velocci S, Minieri M, Pieri M Int J Mol Sci. 2025; 26(5).

PMID: 40076585 PMC: 11900248. DOI: 10.3390/ijms26051960.

References

Truchot A, Raynaud M, Kamar N, Naesens M, Legendre C, Delahousse M . Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction. Kidney Int. 2022; 103(5):936-948. DOI: 10.1016/j.kint.2022.12.011. View

Schachtner T, Reinke P . Estimated Nephron Number of the Donor Kidney: Impact on Allograft Kidney Outcomes. Transplant Proc. 2017; 49(6):1237-1243. DOI: 10.1016/j.transproceed.2017.01.086. View

Korfiatis P, Denic A, Edwards M, Gregory A, Wright D, Mullan A . Automated Segmentation of Kidney Cortex and Medulla in CT Images: A Multisite Evaluation Study. J Am Soc Nephrol. 2021; 33(2):420-430. PMC: 8819990. DOI: 10.1681/ASN.2021030404. View

Chen C, Chen B, Yang J, Li X, Peng X, Feng Y . Development and validation of a practical machine learning model to predict sepsis after liver transplantation. Ann Med. 2023; 55(1):624-633. PMC: 9937004. DOI: 10.1080/07853890.2023.2179104. View

Min S, Papaz T, Lambert A, Allen U, Birk P, Blydt-Hansen T . An Integrated Clinical and Genetic Prediction Model for Tacrolimus Levels in Pediatric Solid Organ Transplant Recipients. Transplantation. 2021; 106(3):597-606. PMC: 8862776. DOI: 10.1097/TP.0000000000003700. View

Fang F, Liu P, Song L, Wagner P, Bartlett D, Ma L . Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning. Front Immunol. 2023; 14:1090373. PMC: 9939643. DOI: 10.3389/fimmu.2023.1090373. View

Garcia-Canadilla P, Sanchez-Martinez S, Marti-Castellote P, Slorach C, Hui W, Piella G . Machine-learning-based exploration to identify remodeling patterns associated with death or heart-transplant in pediatric-dilated cardiomyopathy. J Heart Lung Transplant. 2022; 41(4):516-526. DOI: 10.1016/j.healun.2021.11.020. View

Quinino R, Agena F, Modelli de Andrade L, Furtado M, Chiavegatto Filho A, David-Neto E . A Machine Learning Prediction Model for Immediate Graft Function After Deceased Donor Kidney Transplantation. Transplantation. 2023; 107(6):1380-1389. DOI: 10.1097/TP.0000000000004510. View

Zhou L, Tang L, Song A, Cibrik D, Song P . A LASSO Method to Identify Protein Signature Predicting Post-transplant Renal Graft Survival. Stat Biosci. 2018; 9(2):431-452. PMC: 5793946. DOI: 10.1007/s12561-016-9170-z. View

10.

Elwir S, Lake J . Current Status of Liver Allocation in the United States. Gastroenterol Hepatol (N Y). 2016; 12(3):166-70. PMC: 4872844. View

11.

Halloran K, Parkes M, Timofte I, Snell G, Westall G, Havlin J . Molecular T-cell‒mediated rejection in transbronchial and mucosal lung transplant biopsies is associated with future risk of graft loss. J Heart Lung Transplant. 2020; 39(12):1327-1337. DOI: 10.1016/j.healun.2020.08.013. View

12.

Labriffe M, Woillard J, Gwinner W, Braesen J, Anglicheau D, Rabant M . Machine learning-supported interpretation of kidney graft elementary lesions in combination with clinical data. Am J Transplant. 2022; 22(12):2821-2833. DOI: 10.1111/ajt.17192. View

13.

Yao H, Golbus J, Gryak J, Pagani F, Aaronson K, Najarian K . Identifying potential candidates for advanced heart failure therapies using an interpretable machine learning algorithm. J Heart Lung Transplant. 2022; 41(12):1781-1789. DOI: 10.1016/j.healun.2022.08.028. View

14.

Lau L, Kankanige Y, Rubinstein B, Jones R, Christophi C, Muralidharan V . Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation. Transplantation. 2016; 101(4):e125-e132. PMC: 7228574. DOI: 10.1097/TP.0000000000001600. View

15.

Azhie A, Sharma D, Sheth P, Qazi-Arisar F, Zaya R, Naghibzadeh M . A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study. Lancet Digit Health. 2023; 5(7):e458-e466. DOI: 10.1016/S2589-7500(23)00068-7. View

16.

Halleck F, Diederichs G, Koehlitz T, Slowinski T, Engelken F, Liefeldt L . Volume matters: CT-based renal cortex volume measurement in the evaluation of living kidney donors. Transpl Int. 2013; 26(12):1208-16. DOI: 10.1111/tri.12195. View

17.

Rogers M, Janjua H, Read M, Cios K, Kundu M, Pietrobon R . Recipient Survival after Orthotopic Liver Transplantation: Interpretable Machine Learning Survival Tree Algorithm for Patient-Specific Outcomes. J Am Coll Surg. 2023; 236(4):563-572. DOI: 10.1097/XCS.0000000000000545. View

18.

Zamora-Valdes D, Leal-Leyte P, Kim P, Testa G . Fighting Mortality in the Waiting List: Liver Transplantation in North America, Europe, and Asia. Ann Hepatol. 2017; 16(4):480-486. DOI: 10.5604/01.3001.0010.0271. View

19.

Tong L, Hoffman R, Deshpande S, Wang M . Predicting Heart Rejection Using Histopathological Whole-Slide Imaging and Deep Neural Network with Dropout. IEEE EMBS Int Conf Biomed Health Inform. 2020; 2017. PMC: 7324296. DOI: 10.1109/bhi.2017.7897190. View

20.

Lorenz E, Zaniletti I, Johnson B, Petterson T, Kremers W, Schinstock C . Physiological Age by Artificial Intelligence-Enhanced Electrocardiograms as a Novel Risk Factor of Mortality in Kidney Transplant Candidates. Transplantation. 2023; 107(6):1365-1372. PMC: 10205652. DOI: 10.1097/TP.0000000000004504. View