Machine Learning Applications in Solid Organ Transplantation and Related Complications
Open accessAbstract
The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Several AI algorithms have been developed in a single institution and still need external validation to prove their robustness.
“Several algorithms have been developed in a single institution and still need an external validation to prove their robustness.”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [58]
In some cases, the use of AI cannot provide significant improvements over current models.
“Secondly, in some cases, the use of AI cannot provide significant improvements over current models (58–60).”
From Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation (2022) · cites this paper as [58]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Artificial intelligence in surgery: promises and perils
Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018) · Annals of Surgery · doi:10.1097/sla.0000000000002693 · Cited by 1,468 · see citing works
Komorowski, M., Celi, L. A., Badawi, O., Gordon, A. C., & Faisal, A. A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0213-5 · Cited by 1,290 · see citing works
An interpretable machine learning model for accurate prediction of sepsis in the ICU
Nemati, S., Holder, A., Razmi, F., Stanley, M. D., Clifford, G. D., & Buchman, T. G. (2018) · Critical Care Medicine · doi:10.1097/ccm.0000000000002936 · Cited by 832 · see citing works
Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works
Assessment of Hepatic Steatosis by Expert Pathologists
El‐Badry, A. M., Breitenstein, S., Jochum, W., Washington, K. M., Paradis, V., Rubbia‐Brandt, L., Puhan, M. A., Slankamenac, K., Graf, R., & Clavien, P. (2009) · Annals of Surgery · doi:10.1097/sla.0b013e3181bcd6dd · Cited by 309 · see citing works
Reinforcement learning in artificial and biological systems
Neftci, E., & Averbeck, B. B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0025-4 · Cited by 303 · see citing works
Medicine and the Computer: The Promise and Problems of Change
Schwartz, W. B. (1970) · New England Journal of Medicine · doi:10.1056/nejm197012032832305 · Cited by 227 · see citing works
Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review
Spann, A., Yasodhara, A., Kang, J., Watt, K. D. S., Wang, B., Goldenberg, A., & Bhat, M. (2020) · Hepatology · doi:10.1002/hep.31103 · Cited by 202 · see citing works
Lee, H., Yoon, S. B., Yang, S. M., Kim, W. H., Ryu, H. G., Jung, C.-W., Suh, K., & Lee, K. H. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7110428 · Cited by 197 · see citing works
Halloran, P. F., REEVE, J. P., Akalin, E., Aubert, O., Böhmig, G. A., Brennan, D. C., Bromberg, J. S., Einecke, G., Eskandary, F. A., Gosset, C., et al. (2017) · American Journal of Transplantation · doi:10.1111/ajt.14329 · Cited by 190 · see citing works
REEVE, J. P., Böhmig, G. A., Eskandary, F. A., Einecke, G., Lefaucheur, C., Loupy, A., Halloran, P. F., & the MMDx-Kidney study group (2017) · JCI Insight · doi:10.1172/jci.insight.94197 · Cited by 181 · see citing works
How many TCR clonotypes does a body maintain?
Lythe, G., Callard, R. E., Hoare, R. L., & Molina-París, C. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.10.016 · Cited by 175 · see citing works
Machine-Learning Algorithms Predict Graft Failure After Liver Transplantation
Lau, L., Kankanige, Y., Rubinstein, B. I. P., Jones, R. M., Christophi, C., Muralidharan, V., & Bailey, J. (2016) · Transplantation · doi:10.1097/tp.0000000000001600 · Cited by 170 · see citing works
Shickel, B., Loftus, T. J., Adhikari, L., Ozrazgat-Baslanti, T., Bihorac, A., & Rashidi, P. (2019) · Scientific Reports · doi:10.1038/s41598-019-38491-0 · Cited by 165 · see citing works
Self-citationTang, J., Liu, R., Zhang, Y.-L., Liu, M.-Z., Hu, Y.-F., Shao, M.-J., Zhu, L.-J., Xin, H.-W., Feng, G.-W., Shang, W.-J., et al. (2017) · Scientific Reports · doi:10.1038/srep42192 · Cited by 154 · see citing works
Individualizing liver transplant immunosuppression using a phenotypic personalized medicine platform
Zarrinpar, A., Lee, D.-K., Silva, A., Datta, N. N., Kee, T. W., Eriksen, C. M., Weigle, K., Agopian, V. G., Kaldas, F. M., Farmer, D. G., et al. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aac5954 · Cited by 152 · see citing works
Self-citationBertsimas, D., Kung, J., Trichakis, N., Wang, Y., Hirose, R., & Vagefi, P. A. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15172 · Cited by 141 · see citing works
Learning to diagnose cirrhosis with liver capsule guided ultrasound image classification
Liu, X., Song, J., Wang, S., Zhao, J., & Chen, Y. (2017) · Sensors · doi:10.3390/s17010149 · Cited by 119 · see citing works
REEVE, J. P., Böhmig, G. A., Eskandary, F. A., et al. (2019) · American Journal of Transplantation · doi:10.1111/ajt.15351 · Cited by 115 · see citing works
Current Status of Liver Allocation in the United States.
Elwir, S., & Lake, J. R. (2016) · PubMed · Cited by 83 · see citing works
Miller, P. E., Pawar, S., Vaccaro, B., McCullough, M., Rao, P., Ghosh, R., Warier, P., Desai, N. R., & Ahmad, T. (2019) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2019.01.018 · Cited by 81 · see citing works
Personalized survival predictions via Trees of Predictors: An application to cardiac transplantation
Yoon, J., Zame, W. R., Banerjee, A., Cadeiras, M., Alaa, A. M., & van der Schaar, M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0194985 · Cited by 80 · see citing works
A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
Abdeltawab, H., Shehata, M., Shalaby, A. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-42431-3 · Cited by 76 · see citing works
Kavur, A. E., Gezer, N. S., BARIŞ, M. M., Şahin, Y. H., Özkan, S., Baydar, B., Yuksel, U., Kılıkçıer, Ç., Olut, Ş., Akar, G. B., et al. (2019) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2019.19025 · Cited by 76 · see citing works
Kantidakis, G., Putter, H., Lancia, C., Boer, J. D., Braat, A. E., & Fiocco, M. (2020) · BMC Medical Research Methodology · doi:10.1186/s12874-020-01153-1 · Cited by 75 · see citing works
Deep learning quantification of percent steatosis in donor liver biopsy frozen sections
Sun, L., Marsh, J. N., Matlock, M. K., Chen, L., Gaut, J. P., Brunt, E. M., Swamidass, S. J., & Liu, T. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103029 · Cited by 70 · see citing works
Roy, M., Wang, F., Vo, H., Teng, D., Teodoro, G., Farris, A. B., Castillo-Leon, E., Vos, M. B., & Kong, J. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-0463-y · Cited by 68 · see citing works
Thishya, K., Vattam, K. K., Naushad, S. M., Raju, S. B., & Kutala, V. K. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191921 · Cited by 65 · see citing works
Lee, M. J., Bağcı, P., Kong, J., et al. (2013) · Pathology - Research and Practice · doi:10.1016/j.prp.2013.04.001 · Cited by 64 · see citing works
The Future Role of Machine Learning in Clinical Transplantation
Connor, K. L., O'Sullivan, E. D., Marson, L., Wigmore, S. J., & Harrison, E. M. (2020) · Transplantation · doi:10.1097/tp.0000000000003424 · Cited by 56 · see citing works
Molinari, M., Ayloo, S. M., Tsung, A., Jorgensen, D. R., Tevar, A. D., Rahman, S. H., & Jonassaint, N. L. (2019) · Transplantation · doi:10.1097/tp.0000000000002810 · Cited by 55 · see citing works
Özkan, F., Yavuz, Y. C., İnci, M. F., et al. (2012) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2012.09.013 · Cited by 54 · see citing works
Shousha, H. I., Awad, A., Omran, D., Elnegouly, M., & Mabrouk, M. (2017) · Japanese Journal of Infectious Diseases · doi:10.7883/yoken.jjid.2017.089 · Cited by 47 · see citing works
Barbosa, E. J. M., Lanclus, M., Vos, W. G., Holsbeke, C. S. V., Backer, W. D., Backer, J. D., & Lee, J. (2018) · Academic Radiology · doi:10.1016/j.acra.2018.01.013 · Cited by 40 · see citing works
Yasodhara, A., Dong, V., Azhie, A., Goldenberg, A., & Bhat, M. (2021) · Liver Transplantation · doi:10.1002/lt.25930 · Cited by 37 · see citing works
Boecker, J., Czigány, Z., Bednarsch, J., et al. (2019) · PLoS ONE · doi:10.1371/journal.pone.0214221 · Cited by 29 · see citing works
Liu, P., Tseng, G. C., Wang, Z., et al. (2018) · Human Pathology · doi:10.1016/j.humpath.2018.09.013 · Cited by 23 · see citing works
Recent advances in precision medicine for individualized immunosuppression
Fu, S., & Zarrinpar, A. (2020) · Current Opinion in Organ Transplantation · doi:10.1097/mot.0000000000000771 · Cited by 22 · see citing works
Self-citationAdvances in personalized medicine and noninvasive diagnostics in solid organ transplantation
Maldonado, A. Q., West‐Thielke, P. M., Joyal, K., & Rogers, C. C. (2020) · Pharmacotherapy The Journal of Human Pharmacology and Drug Therapy · doi:10.1002/phar.2484 · Cited by 21 · see citing works
Understanding adaptive immune system as reinforcement learning
Kato, T., & Kobayashi, T. J. (2021) · Physical Review Research · doi:10.1103/physrevresearch.3.013222 · Cited by 15 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial Intelligence: Present and Future Potential for Solid Organ Transplantation
Peloso, A., Moeckli, B., Delaune, V., Oldani, G., Andres, A., & Compagnon, P. (2022) · Transplant International · doi:10.3389/ti.2022.10640 · Cited by 67 · see citing works
- Authors
- Balch, J. A., Delitto, D., Tighe, P. J., Zarrinpar, A., Efron, P. A., Rashidi, P., Upchurch, G. R., Bihorac, A., & Loftus, T. J.
- Affiliations
- University of Florida Health (US)
- Johns Hopkins University (US)
- University of Florida (US)
- Corresponding author
- Loftus, Tyler J.
- ORCID
- Venue
- Frontiers in Immunology
- Publisher
- Frontiers Media SA
- Published
- 2021-09-16
- Volume
- 12
- Pages
- 739728-739728
- ISSN
- 1664-3224 (ISSN-L)
- PMC
- PMC8481939
- OpenAlex
- W3201203768
- Cited by
- 33
- References
- 43
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 3 · 2023: 11 · 2022: 3
- FWCI
- 0.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Immunology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute of General Medical Sciences
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Surgery › Transplantation: Methods and Outcomes
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, solid organ transplantation, transplant complications, clinical decision-making, outcome prediction, radiographic image classification, pharmacokinetics, host immune response, organ allocation, clinical implementation barriers
- MeSH terms
- Clinical Decision-Making, Machine Learning, Decision Support Techniques, Diagnosis, Computer-Assisted, Graft Survival, Humans, Postoperative Complications, Predictive Value of Tests, Risk Factors, Therapy, Computer-Assisted, Time Factors, Organ Transplantation, Treatment Outcome, Risk Assessment, Data Mining
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
