Transplant AI Initiative (TAI)

Giacobbo S, Murray KR, Moayedi Y, et al. Artificial Intelligence for the prognostication and management of heart transplant: A scoping review. The Journal of Heart and Lung Transplantation. 2022;41(4). doi:10.1016/j.healun.2022.01.1688

Brahmbhatt DH, Ross HJ, Moayedi Y. Digital Technology Application for Improved Responses to Health Care Challenges: Lessons Learned From COVID-19. Can J Cardiol. 2022;38(2):279-291. doi:10.1016/j.cjca.2021.11.014

Shin S, Austin PC, Ross HJ, et al. Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality. ESC Heart Fail. 2021;8(1):106-115. doi:10.1002/ehf2.13073

Manlhiot C, van den Eynde J, Kutty S, Ross HJ. A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology. Can J Cardiol. 2022;38(2):169-184. doi:10.1016/j.cjca.2021.11.009

Superina S, Malik A, Moayedi Y, McGillion M, Ross HJ. Digital Health: The Promise and Peril. Can J Cardiol. 2022;38(2):145-148. doi:10.1016/j.cjca.2021.09.033

Cho SM, Austin PC, Ross HJ, et al. Machine Learning Compared With Conventional Statistical Models for Predicting Myocardial Infarction Readmission and Mortality: A Systematic Review. Can J Cardiol. 2021;37(8):1207-1214. doi:10.1016/j.cjca.2021.02.020

Ross HM. Risk of algorithmic bias in contemporary atrial fibrillation research. Cardiovascular Digital Health Journal. 2022;3(4). doi:10.1016/j.cvdhj.2022.07.035

Hearn J, Ross HJ, Mueller B, et al. Neural Networks for Prognostication of Patients With Heart Failure. Circ Heart Fail. 2018;11(8):e005193. doi:10.1161/CIRCHEARTFAILURE.118.005193

Taha K, Ross HJ, Peikari M, et al. An ensemble-based approach to the development of clinical prediction models for future-onset heart failure and coronary artery disease using machine learning. Journal of the American College of Cardiology. 2020;75(11):2046. doi:10.1016/s0735-1097(20)32673-5

Aleksova N, Alba AC, Molinero VM, et al. Risk prediction models for survival after heart transplantation: A systematic review. Am J Transplant. 2020;20(4):1137-1151. doi:10.1111/ajt.15708

Nolan RP, Peikari M, Fan C-PS, Fezza GC, Ross HJ, McIntosh C. A machine learning analysis of therapeutic components of digital counseling for heart failure. Journal of the American College of Cardiology. 2022;79(9):2044. doi:10.1016/s0735-1097(22)03035-2

Afshar M, Lee DS, Epelman S, Gramolini AO, Ross HJ, Lawler PR. Next-Generation Approaches to Predicting the Need for Heart Failure Hospitalization. Can J Cardiol. 2019;35(4):379-381. doi:10.1016/j.cjca.2019.01.008

Lee D, Cho SM, Austin P, et al. Comparison of machine learning (ML) methods with conventional statistical models (CSM) for prediction of mortality in myocardial infarction (MI) patients. Journal of the American College of Cardiology. 2020;75(11):3538. doi:10.1016/s0735-1097(20)34165-6

Shin S, Austin PC, Ross HJ, et al. Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality. ESC Heart Failure. 2020;8(1):106-115. doi:10.1002/ehf2.13073

Azhie A, Sharma D, Sheth P, et al. A deep learning framework for personalised dynamic diagnosis of graft fibrosis after liver transplantation: a retrospective, single Canadian centre, longitudinal study [published online ahead of print, 2023 May 18]. Lancet Digit Health. 2023;S2589-7500(23)00068-7. doi:10.1016/S2589-7500(23)00068-7

Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning,and deep learning in liver transplantation. J Hepatol. 2023;78(6):1216-1233. doi:10.1016/j.jhep.2023.01.006

Baciu C, Xu C, Alim M, Prayitno K, Bhat M. Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions. Front Artif Intell. 2022;5:1050439. Published 2022 Nov 15. doi:10.3389/frai.2022.1050439

Gotlieb N, Azhie A, Sharma D, et al. The promise of machine learning applications in solid organ transplantation. NPJ Digit Med. 2022;5(1):89. Published 2022 Jul 11. doi:10.1038/s41746-022-00637-2

Tran J, Sharma D, Gotlieb N, Xu W, Bhat M. Application of machine learning in liver transplantation: a review. Hepatol Int. 2022;16(3):495-508. doi:10.1007/s12072-021-10291-7

Bhat M, Rabindranath M. The promise of artificial intelligence for predictive biomarkers in hepatology. Hepatol Int. 2022;16(3):523-525. doi:10.1007/s12072-022-10342-7

Cooper M, Krishnan RG, Bhat M. Deep learning and the future of the Model for End-Stage Liver Disease-sodium score. Liver Transpl. 2022;28(7):1128-1130. doi:10.1002/lt.26485

Sarvestany SS, Kwong JC, Azhie A, et al. Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study. Lancet Digit Health. 2022;4(3):e188-e199. doi:10.1016/S2589-7500(21)00270-3

Sharma D, Gotlieb N, Farkouh ME, Patel K, Xu W, Bhat M. Machine Learning Approach to Classify Cardiovascular Disease in Patients With Nonalcoholic Fatty Liver Disease in the UK Biobank Cohort. J Am Heart Assoc. 2022;11(1):e022576. doi:10.1161/JAHA.121.022576

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(5):e295-e305. doi:10.1016/S2589-7500(21)00040-6

Yasodhara A, Dong V, Azhie A, Goldenberg A, Bhat M. Identifying Modifiable Predictors of Long-Term Survival in Liver Transplant Recipients With Diabetes Mellitus Using Machine Learning. Liver Transpl. 2021;27(4):536-547. doi:10.1002/lt.25930

Spann A, Yasodhara A, Kang J, et al. Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review. Hepatology. 2020;71(3):1093-1105. doi:10.1002/hep.31103

Bhat V, Tazari M, Watt KD, Bhat M. New-Onset Diabetes and Preexisting Diabetes Are Associated With Comparable Reduction in Long-Term Survival After Liver Transplant: A Machine Learning Approach. Mayo Clin Proc. 2018;93(12):1794-1802. doi:10.1016/j.mayocp.2018.06.020

Ivanics T, Patel MS, Erdman L, Sapisochin G. Artificial intelligence in transplantation (machine-learning classifiers and transplant oncology). Curr Opin Organ Transplant. 2020;25(4):426-434. doi:10.1097/MOT.0000000000000773

Ivanics T, Nelson W, Patel MS, et al. The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach. Liver Transpl. 2022;28(4):593-602. doi:10.1002/lt.26332

Ivanics T, So D, Claasen MPAW, et al. Machine learning-based mortality prediction models using national liver transplantation registries are feasible but have limited utility across countries. Am J Transplant. 2023;23(1):64-71. doi:10.1016/j.ajt.2022.12.002

Chao BT, Ma J, Zhou X, et al. (251) a machine learning approach to processing and interpreting ex vivo lung radiographs predicts transplant outcomes. The Journal of Heart and Lung Transplantation. 2023;42(4). doi:10.1016/j.healun.2023.02.1555

Berra G, Farkona S, Mohammed-Ali Z, et al. Association between the renin-angiotensin system and chronic lung allograft dysfunction. Eur Respir J. 2021;58(4):2002975. Published 2021 Oct 21. doi:10.1183/13993003.02975-2020

Rabindranath M, Naghibzadeh M, Zhao X, Holdsworth S, Brudno M, Sidhu A, Bhat M. Clinical Deployment of Machine Learning Tools in Transplant Medicine: What Does the Future Hold? Transplantation. 2024 Aug 1;108(8):1700-1708. doi: 10.1097/TP.0000000000004876. Epub 2023 Dec 7. PMID: 39042768.

Bhat M, Rabindranath M, Chara BS, Simonetto DA. Artificial intelligence, machine learning, and deep learning in liver transplantation. J Hepatol. 2023 Jun;78(6):1216-1233. doi: 10.1016/j.jhep.2023.01.006

Deeb M, Gangadhar A, Rabindranath M, Rao K, Brudno M, Sidhu A, Wang B, Bhat M. The emerging role of generative artificial intelligence in transplant medicine. Am J Transplant. 2024 Jun 18:S1600-6135(24)00382-4. doi: 10.1016/j.ajt.2024.06.009.