ArticleFrontiers in cardiovascular medicine2021
A Systematic Quality Scoring Analysis to Assess Automated Cardiovascular Magnetic Resonance Segmentation Algorithms.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Myocardial Deformation Analysis in Congenital Aortic Valve Stenosis: Comparison Across Clinical Phenotypes in Children.Pediatric cardiology · 2026Article
- Review of the Current State of Artificial Intelligence in Pediatric Cardiovascular Magnetic Resonance Imaging.Children (Basel, Switzerland) · 2025Review
- Quality control of cardiac magnetic resonance imaging segmentation, feature tracking, aortic flow, and native T1 analysis using automated batch processing in the UK Biobank study.European heart journal. Imaging methods and practice · 2024Article
- Performance of artificial intelligence for biventricular cardiovascular magnetic resonance volumetric analysis in the clinical setting.The international journal of cardiovascular imaging · 2022Article
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Authors and funding
17 authors.
Funding
Abstract
backgroundThe quantitative measures used to assess the performance of automated methods often do not reflect the clinical acceptability of contouring. A quality-based assessment of automated cardiac magnetic resonance (CMR) segmentation more relevant to clinical practice is therefore needed.
objectiveWe propose a new method for assessing the quality of machine learning (ML) outputs. We evaluate the clinical utility of the proposed method as it is employed to systematically analyse the quality of an automated contouring algorithm.
methodsA dataset of short-axis (SAX) cine CMR images from a clinically heterogeneous population (
resultsThe overall distribution of scores between the two methods was significantly different, with automated contours scoring better than the manual (OR (95% CI) = 1.17 (1.07-1.28),
conclusionsOur results confirm the ability of our systematic scoring analysis to determine the clinical acceptability of automated contours. This approach focused on the contours' clinical utility could ultimately improve clinicians' confidence in artificial intelligence and its acceptability in the clinical workflow.
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