Precision, prognosis, and clinical performance of rounded and trabecular segmentation of cine cardiovascular magnetic resonance
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Le résumé fourni par la source
BACKGROUND: Measurements of cardiac size and function drive clinical decisions. Left ventricular (LV) metrics can be derived from cardiac MR images by delineating the blood pool and myocardium, by either drawing a rounded contour to approximate the compacted myocardial border, or by delineating the papillary muscles and trabeculae (trabecular segmentation). There is no consensus as to which is best, particularly in the emergent AI era. We developed machine-learning (ML) approaches for both and compared them for clinically important metrics (error rate, precision, and prognosis). METHODS: Separate ML models were developed for rounded and trabecular segmentation, using U-net models trained on 1923 subjects (mixed pathology, multiple scanners, multiple centers). Blood and myocardial volumes for each segmentation method were compared on 4118 healthy UK biobank subjects. Model segmentation quality was evaluated subjectively on a real-world clinical dataset of 1594 consecutive CMR scans, with all scans included regardless of image quality and artifacts. Scan-rescan precision was measured on a multi-center, multi-disease dataset of 109 subjects scanned twice and compared to human performance. Finally, prognostication ability was evaluated on 1215 clinical patients, using a primary outcome of all-cause mortality and hospitalization with heart failure. RESULTS: Error rates (where a human disagreed by >1 mL) were the same, occurring in 0.6% (184/29680) of images and 3.6% (60/1594) of patients. In health, the mean EF was 4% higher for trabecular vs rounded segmentation. On test-retest data, there was no difference between rounded and trabecular ML models for precision, apart from end-diastolic and end-systolic volume, which was better for rounded segmentations. ML rounded and trabecular precision exceeded clinician performance for EF. There were marginal differences in prognostication between rounded and trabecular models. CONCLUSION: We developed an automated method for annotating papillary muscles and trabeculae from cardiac MR images with low error rates. We found higher precision than clinicians in ejection fraction. There was similar precision and prognostication to an ML rounded model with similarly low error rates. Findings support the feasibility of automated trabecular segmentation in clinical care and clinical trials.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Precision, prognosis, and clinical performance of rounded and trabecular segmentation of cine cardiovascular magnetic resonance
- Date Crossref
- 01/01/2026
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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St Bartholomew's Hospital Barts Heart Centre pays non établi dans la noticeÉtablissement de santé
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University College London Institute of Cardiovascular Sciences pays non établi dans la noticeUniversité ou école supérieure
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Microsoft (United States) pays non établi dans la noticeEntreprise
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Futures Group (United States) pays non établi dans la noticeEntreprise
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National Institutes of Health pays non établi dans la noticeOrganisme public
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and Blood Institute Lung pays non établi dans la noticeStructure de recherche
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Allina Health pays non établi dans la noticeÉtablissement de santé
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United Hospital pays non établi dans la noticeÉtablissement de santé
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Health Futures pays non établi dans la noticeInstitution
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University of Pittsburgh Medical Center Allina Health Minneapolis Heart Institute at United Hospital pays non établi dans la noticeUniversité ou école supérieure
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University of Pittsburgh Medical Centre Allina Health Minneapolis Heart Institute at United Hospital pays non établi dans la noticeUniversité ou école supérieure
Barts Heart Centre — St Bartholomew's Hospital, Institute of Cardiovascular Sciences — University College London et Microsoft (United States), avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.