Accurate assessment of LV function using the first automated 2D-border detection algorithm for small animals - evaluation and application to models of LV dysfunction
Bibliographic record
Abstract
Echocardiography is the most commonly applied technique for non-invasive assessment of cardiac function in small animals. Manual tracing of endocardial borders is time consuming and varies with operator experience. Therefore, we aimed to evaluate a novel automated two-dimensional software algorithm (Auto2DE) for small animals and compare it to the standard use of manual 2D-echocardiographic assessment (2DE). We hypothesized that novel Auto2DE will provide rapid and robust data sets, which are in agreement with manually assessed data of animals.2DE and Auto2DE were carried out using a high-resolution imaging-system for small animals. First, validation cohorts of mouse and rat cine loops were used to compare Auto2DE against 2DE. These data were stratified for image quality by a blinded expert in small animal imaging. Second, we evaluated 2DE and Auto2DE in four mouse models and four rat models with different cardiac pathologies.Automated assessment of LV function by 2DE was faster than conventional 2DE analysis and independent of operator experience levels. The accuracy of Auto2DE-assessed data in healthy mice was dependent on cine loop quality, with excellent agreement between Auto2DE and 2DE in cine loops with adequate quality. Auto2DE allowed for valid detection of impaired cardiac function in animal models with pronounced cardiac phenotypes, but yielded poor performance in diabetic animal models independent of image quality.Auto2DE represents a novel automated analysis tool for rapid assessment of LV function, which is suitable for data acquisition in studies with good and very good echocardiographic image quality, but presents systematic problems in specific pathologies.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".