ArticleEuropean heart journal. Digital health2023
Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning.
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 22 papers.
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Who cites it
22 citing papers in PubMed.
- Non-invasive imaging in heart transplant recipients: state of art and future direction.European journal of nuclear medicine and molecular imaging · 2026Review
- Artificial intelligence and machine learning in heart and lung transplantation.EClinicalMedicine · 2026Review
- Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review.Frontiers in cardiovascular medicine · 2026Review
- Histopathological evaluation of abdominal aortic aneurysms with deep learning.Diagnostic pathology · 2025Article
- An In-depth overview of artificial intelligence (AI) tool utilization across diverse phases of organ transplantation.Journal of translational medicine · 2025Review
- Decoding pathology: the role of computational pathology in research and diagnostics.Pflugers Archiv : European journal of physiology · 2025Review
- The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease.Biomedicines · 2025Review
- Deep learning helps discriminate between autoimmune hepatitis and primary biliary cholangitis.JHEP reports : innovation in hepatology · 2025Article
- Weakly Supervised Deep Learning Can Analyze Focal Liver Lesions in Contrast-Enhanced Ultrasound.Digestion · 2025Article
- Deep-learning based morphological segmentation of canine diffuse large B-cell lymphoma.Frontiers in veterinary science · 2025Article
- Understanding Machine Learning Applications in Lung Transplantation: A Narrative Review.Transplant international : official journal of the European Society for Organ Transplantation · 2025Review
- Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative.Journal of pathology informatics · 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
- Cardiac Allograft Vasculopathy: Challenges and Advances in Invasive and Non-Invasive Diagnostic Modalities.Journal of cardiovascular development and disease · 2024Review
- A Preliminary Study Assessing a Transfer Learning Approach to Intestinal Image Analysis to Help Determine Treatment Response in Canine Protein-Losing Enteropathy.Veterinary sciences · 2024Article
- Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.Nature communications · 2024Article
- Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma.Nature communications · 2023Article
- Prediction models for hormone receptor status in female breast cancer do not extend to males: further evidence of sex-based disparity in breast cancer.NPJ breast cancer · 2023Article
- Deep learning-based subtyping of gastric cancer histology predicts clinical outcome: a multi-institutional retrospective study.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2023Article
- Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study.Cell reports. Medicine · 2023Article
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aims: One of the most important complications of heart transplantation is organ rejection, which is diagnosed on endomyocardial biopsies by pathologists. Computer-based systems could assist in the diagnostic process and potentially improve reproducibility. Here, we evaluated the feasibility of using deep learning in predicting the degree of cellular rejection from pathology slides as defined by the International Society for Heart and Lung Transplantation (ISHLT) grading system. Methods and results: We collected 1079 histopathology slides from 325 patients from three transplant centres in Germany. We trained an attention-based deep neural network to predict rejection in the primary cohort and evaluated its performance using cross-validation and by deploying it to three cohorts. For binary prediction (rejection yes/no), the mean area under the receiver operating curve (AUROC) was 0.849 in the cross-validated experiment and 0.734, 0.729, and 0.716 in external validation cohorts. For a prediction of the ISHLT grade (0R, 1R, 2/3R), AUROCs were 0.835, 0.633, and 0.905 in the cross-validated experiment and 0.764, 0.597, and 0.913; 0.631, 0.633, and 0.682; and 0.722, 0.601, and 0.805 in the validation cohorts, respectively. The predictions of the artificial intelligence model were interpretable by human experts and highlighted plausible morphological patterns. Conclusion: We conclude that artificial intelligence can detect patterns of cellular transplant rejection in routine pathology, even when trained on small cohorts.
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