Accuracy of speckle tracking in the context of stress echocardiography in short axis view: An in vitro validation study
Bibliographic record
Abstract
AIM: This study aimed to test the accuracy of a speckle tracking algorithm to assess myocardial deformation in a large range of heart rates and strain magnitudes compared to sonomicrometry. METHODS AND RESULTS: Using a tissue-mimicking phantom with cyclic radial deformation, radial strain derived from speckle tracking (RS-SpT) of the upper segment was assessed in short axis view by conventional echocardiography (Vivid q, GE) and post-processed with clinical software (EchoPAC, GE). RS-SpT was compared with radial strain measured simultaneously by sonomicrometers (RS-SN). Radial strain was assessed with increasing deformation rates (60 to 160 beats/min) and increasing pulsed volumes (50 to 100 ml/beat) to simulate physiological changes occurring during stress echocardiography. There was a significant correlation (R2 = 0.978, P <0.001) and a close agreement (bias ± 2SD, 0.39 ± 1.5%) between RS-SpT and RS-SN. For low strain values (<15%), speckle tracking showed a small but significant overestimation of radial strain compared to sonomicrometers. Two-way analysis of variance did not show any significant effect of the deformation rate. For RS-SpT, the feasibility was excellent and the intra- and inter-observer variability were low (the intraclass correlation coefficients were 0.96 and 0.97, respectively). CONCLUSIONS: Speckle tracking demonstrated a good correlation with sonomicrometry for the assessment of radial strain independently of the heart rate and strain magnitude in a physiological range of values. Though speckle tracking seems to be a reliable and reproducible technique to assess myocardial deformation variations during stress echocardiography, further studies are mandated to analyze the impact of angulated and artefactual out-of-plane motions and inter-vendor variability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".