Comparison of semi-automatic cardiac analysis software for the evaluation of the left ventricular ejection fraction in small animal PET
Bibliographic record
Abstract
1792 Objectives Several software packages have been developed to compute the left ventricular ejection fraction (EF). All approaches implement some prior allowing the user to start calculation with an initial target. In this study, we compare the EF obtained in small animals from three semi-automatic programs: MI-Heart (MI), Quantitative PET (QPET), and Corridor4DM (4DM), as well as manual tracing. Methods The dataset contained images of 30 rats and 33 mice with various health conditions: 2 infarcted, 14 aortic regurgitation, 26 heart failure, and 21 healthy controls. Images were recorded with three different radiotracers (18F-FDG, 18F-FTHA and 11C-Acetate) on LabPET scanners. Two observers processed the images up to three times with each software, except QPET used only once each since images were already reoriented. Manual tracing was performed to provide the EF reference standard (STD). Bland-Altman plots and correlation were used to demonstrate the difference and limits of agreement between observers. Results MI-Heart had an average bias relative to STD of -9.5% for rats and -5.3% for mice, while QPET and 4DM had a bias of -19.1% and -19.6% for rats and +9.4% and +10.5% for mice, respectively. A good correlation of individual EF data (r > 0.7) was observed between software for rats as well as for mice. Test-retest repeatability was similar between observers, assessed by the coefficients of repeatability (CR): 7.6% MI, 6.1% 4DM and 6.2% QPET for rat and 13.6% MI, 11.9% 4DM and 12.5% QPET for mice. Conclusions All three software yielded less variable results with less bias relative to STD on mice than on rats. Overall, the dedicated MI-Heart program performed as well as adapted clinical software to evaluate the ejection fraction in small animals.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".