Assessment of myocardial deformation with CMR: a comparison with ultrasound speckle tracking in a cohort of highly trained endurance athletes
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Plan Nacional I.D., Del Programa Estatal de Fomento De La Investigación Científica y Técnica de Excelencia, Subprograma De Generación Del Conocimiento, Ministerio de Economía y Competitividad 2013. Background Myocardial deformation integrated with cardiac dimensions provides a comprehensive assessment of the ventricular remodelling patterns induced by cumulative effects of intensive exercise. Feature tracking(FT) can measure myocardial deformation from cardiac magnetic resonance(CMR) cine sequences; however, its accuracy is still scarcely validated. Purpose Our aim was to compare FT’s accuracy and reproducibility to speckle tracking echocardiography (STE) in highly trained endurance athletes (EAs). Methods 93 EAs (>12 hours training/week during the last 5 years, 52% male, 35 ± 5.1 years) and 72 age-matched controls underwent a resting CMR and a transthoracic echocardiography to assess biventricular exercise-induced remodelling and biventricular global longitudinal strain (GLS) by CMR-FT and STE. Results High endurance training load was associated with larger bi-ventricular and bi-atrial sizes and mildly reduced systolic function of both ventricles (p < 0,05). Strain values (both by CMR-FT and STE) proportionally decreased with increasing ventricular volumes potentially depicting the increased volume and functional biventricular reserve that characterize EAs heart. Strain values were lower when assessed by CMR-FT as compared to STE (p < 0.001), with good reproducibility for the LV (bias = 3.94%, LOA= ± 4.27%) but wider variability for RV strains (Figure 2). Conclusions Biventricular longitudinal strain values were lower when assessed by FT compared to STE. Both methods were comparable when measuring LV strain but not RV strain. These differences might be justified by FT’s lower in-plane spatial and temporal resolution, which is particularly relevant for the complex anatomy of the RV. Abstract Figure. Fig 1. Bland-Altman plots; FT vs STE.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".