Artificial intelligence implementation in automated heart chambers quantification during pharmacological stress echocardiography
Bibliographic record
Abstract
Aims: Stress echocardiography (SE) is widely used for assessing coronary artery disease, but volumetric chamber analysis during SE is limited by time-consuming manual tracings and operator-dependent variability. Automated evaluation may overcome these barriers and enhance efficiency. Methods and results: This multi-centre study included 240 participants undergoing pharmacological SE for ischaemic heart disease evaluation from five sites in four countries. SE imaging data from apical four-chamber and two-chamber views were acquired during rest and stress phases. Expert cardiologists manually traced endocardial borders for left ventricular (LV), left atrial (LA) and right ventricular (RV), right atrial (RA) areas, which were compared to machine learning (ML) derived measurements. Image quality was categorized as optimal, good, fair, or poor, and its influence on ML performance was analysed. Statistical methods included Intraclass Correlation Coefficients (ICCs), Bland-Altman testing, and within-patient coefficient of variation. The yield of the ML algorithm demonstrated consistency across rest and stress phases. It demonstrated strong agreement with cardiologists for LV and LA volumes, with ICCs ranging from 0.84 to 0.93 across rest and stress conditions. RA and RV areas measurements showed moderate correlations, with better agreement at rest than during stress phases. Image quality significantly influenced ML performance, as poor-quality images reduced diagnostic yield. Conclusion: AI-driven volumetric analysis is a reliable method for quantifying left-sided heart chambers during pharmacological SE, with results closely matching expert measurements. Moderate reliability for right-sided chambers highlights the need for high-quality imaging and standardized protocols. AI integration may streamline SE workflows and support improved clinical decision-making.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".