Transplant AI Initiative (TAI)

With the confluence of AI expertise in Toronto and the largest transplant program in North America, we are working toward leading the world in AI tool development and implementing these tools in the clinical setting.

Our Goals

Our Transplant AI initiative is based on a solid foundation of clinical knowledge, computer science and data analytics and will facilitate advancements in the four major fields of interest:

Donor/recipient matching

Donor selection is a challenging and multifactorial decision influenced by both donor and recipient factors as well as match considerations.
Machine learning models can analyze a wide range of donor and recipient characteristics and detect complex nonlinear relationships between input variables to identify the most compatible matches.
We intend to use machine learning to optimize donor-recipient matching and increase post-transplant survival.

Prioritization of organ allocation

Allocation process is complex and involves many variables.
AI can help optimize the allocation process by uncovering non-linear and subtle correlations among these variables that cannot be identified using conventional analysis models.
AI has the potential to improve efficiency and fairness of organ allocation process.
AI can be used to develop predictive models that can identify patients who are at highest risk of mortality or dropout.

Long-term post-transplant complications

Long-term survival compromised by cardiovascular, cancer, infection and graft failure-associated mortality.
AI trained on large datasets can identify patterns and associations to predict outcomes.
Opportunity for individualized prevention or intervention plan for transplant recipients based on the top-ranked modifiable features.

Equity for organ allocation

Inequities in organ allocation due to various factors such as sex, gender, race, ethnicity, socioeconomic status, and access to healthcare.
AI could help address inequities in organ allocation process by enabling more accurate and efficient organ matching.

Our Core Values

Collaboration

Partner with patients, health teams, researchers and industry to improve the success of solid organ transplantation and the quality of life for transplant recipients.

Communication & Transparency

Raise awareness and share knowledge regarding transplant research and its advancements.

Research and Innovation

By leveraging AI technologies, we aim to optimize organ allocation, improve patient outcomes and increase the efficiency of the transplant process.

Leadership

Guide the development and implementation of machine learning models and other AI techniques to improve outcomes for transplant patients.

Equity & Advocacy

Develop strategies to improve quality and quantity of life of our patients regardless of sex, race, gender, or other such factors and move towards a more equitable care.

Research Fellowship in Transplant AI

We are always open to applications from enthusiastic Transplant Clinical Research Fellows and Computer Science/Engineering graduate students/postdoctoral fellows wanting to do training in Transplant AI! As a leading centre in this area, you will have the unique opportunity to contribute to the development and deployment of machine learning tools into the transplant clinical setting.

Please send us your CV and letter of application to mamatha.bhat@uhn.ca and aman.sidhu@uhn.ca if interested!

Publications

Explore our pioneering publications on leveraging Machine Learning and Artificial Intelligence in the transformative field of organ transplantation. Discover how these cutting-edge technologies are redefining possibilities, enhancing precision and improving outcomes in transplant medicine

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

News & Events

2023 Transplant AI Symposium: A Vision for the Future

We are immensely grateful to everyone who joined us for our recent symposium, where we explored the future of transplantation and artificial intelligence. Our event brought together leading experts in the field to discuss the latest advancements and breakthroughs. Your engagement and enthusiasm significantly contributed to the success of our symposium.

If you're interested in revisiting the insights shared during our event or if you missed any sessions, we're more than willing to share a copy of the recorded session. Please reach out to Elisa Pasini at Elisa.Pasini@uhn.ca to request your copy. We believe it will serve as a valuable resource for further reflection and insights.