ArticleEuropean heart journal. Digital health2023
Non-invasive detection of cardiac allograft rejection among heart transplant recipients using an electrocardiogram based deep learning model.
Article in European heart journal. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Speckle-tracking echocardiography of left and right ventricle and acute cellular rejection in orthotropic heart transplantation: a systematic review and meta-analysis.The international journal of cardiovascular imaging · 2025Pooled it
- Non-invasive imaging in heart transplant recipients: state of art and future direction.European journal of nuclear medicine and molecular imaging · 2026Review
- Multiparametric cardiac MRI in the diagnosis of transplant rejection in patients after orthotopic heart transplantation.European radiology · 2026Article
- Protocol for a global electronic Delphi on integrating artificial intelligence into solid organ transplantation.World journal of transplantation · 2026Review
- AI-Assisted Decision-Making for End-Stage Organ Failure: Opportunities and Ethical Concerns.Artificial organs · 2026Review
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
- Review
- Clinical relevance and outcome of routine endomyocardial biopsy to detect rejection after heart transplantation.JHLT open · 2025Article
- Ethics and Algorithms to Navigate AI's Emerging Role in Organ Transplantation.Journal of clinical medicine · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- 2024 transplant AI symposium: key AI models shaping the future of transplant care.Frontiers in transplantation · 2025Article
- Diagnosis of rejection following heart transplantation: diving into the future.Frontiers in transplantation · 2025Review
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- [First experience in Mexico with plasmapheresis and rituximab treating humoral rejection in heart transplant].Archivos de cardiologia de Mexico · 2024Article
- What Else Can AI See in a Digital ECG?Journal of personalized medicine · 2023Review
- ECG Features in Orthotopic Cardiac Xenotransplantation: Comparisons With Published Literature.XenotransplantationArticle
Corrections and comments
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Authors and funding
25 authors at 4 institutions in 1 country.
Funding
Abstract
Aims: Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results: Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set ( Conclusion: An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.