- Titel
- Prediction of heart transplant rejection from routine pathology slides with self-supervised deep learning
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-976630
- Quellenangabe
- European heart journal - digital health
Erscheinungsjahr: 2023
Jahrgang: 4
Heft: 3
Seiten: 265-274
E-ISSN: 2634-3916 - Erstveröffentlichung
- 2023
- Abstract (EN)
- 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.
- Andere Ausgabe
- Link zum Artikel, der zuerst in der Zeitschrift „European heart journal - digital health” im Verlag Oxford University Press erschienen ist.
DOI: 10.1093/ehjdh/ztad016 - Verweis
- Ergänzendes Material ist unter folgendem Link zu finden.
Link: https://academic.oup.com/ehjdh/article/4/3/265/7067962#supplementary-data - Freie Schlagwörter (EN)
- Computational pathology, Artificial intelligence, Self-supervised deep learning, Multiple instance learning, Cardiac allograft rejection
- Klassifikation (DDC)
- 610
- Verlag
- Oxford University Press, Oxford
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-976630
- Veröffentlichungsdatum Qucosa
- 06.11.2025
- Dokumenttyp
- Artikel
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0