ArticleNature medicine2022
Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.
Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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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
49 citing papers in PubMed, 89 citations in OpenAlex.
- Non-invasive imaging in heart transplant recipients: state of art and future direction.European journal of nuclear medicine and molecular imaging · 2026Review
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- Accurate pathologic classification of aggressive myocarditis improves insights into patient precise medicine.Fundamental research · 2026Article
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Deep-learning triage of three-dimensional pathology datasets for comprehensive and efficient pathologist assessments.Nature biomedical engineering · 2026Article
- Generalist foundation models from a multimodal dataset for 3D computed tomography.Nature biomedical engineering · 2026Article
- CLARISA: Connexin-43 Lateralization Automated ROI-Based Image Signal Analyzer.International journal of molecular sciences · 2026Article
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Building foundation models for cardiac MRI.Nature biomedical engineering · 2026Article
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
- Treatment of antibody-mediated rejection in heart transplantation: from empirical combination to multi-target precision intervention.Frontiers in immunology · 2026Review
- PathOrchestra: a comprehensive foundation model for computational pathology with over 100 diverse clinical-grade tasks.NPJ digital medicine · 2025Article
- A multimodal whole-slide foundation model for pathology.Nature medicine · 2025Article
- Advancing open-source visual analytics in digital pathology: A systematic review of tools, trends, and clinical applications.Journal of pathology informatics · 2025Review
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Computational pathology assessments of cardiac stromal remodeling: Clinical correlates and prognostic implications in heart transplantation.JHLT open · 2025Article
- Follicular lymphoma research: an open dialogue for a collaborative roadmap.Histopathology · 2025Review
- Core signature of rejection-associated cytokines and chemokines in endomyocardial biopsies after heart transplantation.Frontiers in cardiovascular medicine · 2025Article
- Explainable AI for computational pathology identifies model limitations and tissue biomarkers.ArXiv · 2024Article
Corrections and comments
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Authors and funding
20 authors at 6 institutions in 3 countries.
Funding
Abstract
Endomyocardial biopsy (EMB) screening represents the standard of care for detecting allograft rejections after heart transplant. Manual interpretation of EMBs is affected by substantial interobserver and intraobserver variability, which often leads to inappropriate treatment with immunosuppressive drugs, unnecessary follow-up biopsies and poor transplant outcomes. Here we present a deep learning-based artificial intelligence (AI) system for automated assessment of gigapixel whole-slide images obtained from EMBs, which simultaneously addresses detection, subtyping and grading of allograft rejection. To assess model performance, we curated a large dataset from the United States, as well as independent test cohorts from Turkey and Switzerland, which includes large-scale variability across populations, sample preparations and slide scanning instrumentation. The model detects allograft rejection with an area under the receiver operating characteristic curve (AUC) of 0.962; assesses the cellular and antibody-mediated rejection type with AUCs of 0.958 and 0.874, respectively; detects Quilty B lesions, benign mimics of rejection, with an AUC of 0.939; and differentiates between low-grade and high-grade rejections with an AUC of 0.833. In a human reader study, the AI system showed non-inferior performance to conventional assessment and reduced interobserver variability and assessment time. This robust evaluation of cardiac allograft rejection paves the way for clinical trials to establish the efficacy of AI-assisted EMB assessment and its potential for improving heart transplant outcomes.
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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 OpenQuestion graph.