Abstract 14753: Impact of Automation on Time Burden of Echocardiographic LVEF Measurements: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Impact of time savings with the use of automated left ventricular volume measurements has not been systematically quantified. Hypothesis: Automated measurement of left ventricular volumes will lead to significant time savings. Methods: Electronic search of MEDLINE and EMBASE was performed. Inclusion criteria included 1) LVEF (either 2D or 3D) quantification using completely automated software, 2) comparator group of manual measurements and 3) stated mean and standard deviation of measurement time. Meta-analysis was performed with studies weighted by DerSimonian-Laird method and pooled using random-effects model. Results: 6 studies of automated 3D-LVEF measurement were identified with total of 697 pts. Time savings for 3D-LVEF automation was -371.0 seconds per study (95%CI -754.6 to +12.6 seconds, p = 0.058, Q (df=5) 2104.3, I2 99.9%), which was non-significant. HeartModel (Phillips) was used in 4 studies with 550 patients with non-significant time savings of -452.3 seconds (95%CI -1029.5 to +124.9 seconds, p=0.12, Q(df=3) 1954.1, I2 99.9%). 4D AutoLVQ (GE) was used in 1 study of 103 patients with significant time savings (142±30 sec vs. 226±114 sec, p<0.001). A single study of 44 pts used eSieLVA(Siemens) with significant time savings (37 ± 8 sec vs 371 ± 116 sec, p<0.001). For automated 2D-LVEF measurements, 6 studies were identified with total of 681 pts with total time savings with automation of -65.8 seconds, (95%CI -86.3 to -45.3 seconds, p < 0.001, Q(df=5) 86.1, I 2 98.7%). AutoEF (GE) was used in 3 studies with total of 230 pts with time-savings of -49.6 seconds (95%CI -59.6 to -39.7, p<0.0001, Q(df=2) 15.3, I2 88.2%). Auto EF (Siemens) was used in 2 studies, total 267 pts, with time savings of -75.2 seconds, (95%CI -118.3 to -32.1 sec, p=0.0006, Q(df=1) 24.7, I2 96.0%). A2DQ(Phillips) was used in 1 study of 184 pts (41 ± 5 sec vs. 98 ± 8 sec, p<0.001). Subgroup analysis did not reveal significant difference in time savings between 2D-LVEF software (p=0.26). Conclusions: Significant time savings were observed for automated 2D-LVEF measurements with nonsignificant trend for automated 3D-LVEF measurements. Adoption of automated LVEF measurements may contribute to improved echocardiography lab efficiency.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".