Deep learning-enabled assessment of cardiac allograft rejection from endomyocardial biopsies.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
received:
15
01
2021
accepted:
19
01
2022
entrez:
22
3
2022
pubmed:
23
3
2022
medline:
27
4
2022
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 35314822
doi: 10.1038/s41591-022-01709-2
pii: 10.1038/s41591-022-01709-2
pmc: PMC9353336
mid: NIHMS1825956
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
575-582Subventions
Organisme : NIGMS NIH HHS
ID : R35 GM138216
Pays : United States
Organisme : NLM NIH HHS
ID : T15 LM007092
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA251062
Pays : United States
Organisme : NHGRI NIH HHS
ID : T32 HG002295
Pays : United States
Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.
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