Bibliographic record
Abstract
Cardiovascular diseases are one of the leading causes of death globally. Non-invasive echocardiogram imaging is widely used by clinicians to assess and diagnose cardiac disease. Advances in deep learning have created tools to help clinicians analyze echocardiograms and therefore enhance diagnosis and monitoring of cardiac disease. This work focuses on the application of machine learning on echocardiogram videos for two different tasks; left ventricular ejection fraction (LVEF) estimation and patent ductus arteriosus (PDA) segmentation and identification. The focus of this work is to create deep learning based classification frameworks with interpretable intermediate results by incorporating segmentation architectures into both classification tasks. The proposed LVEF classification model uses LV segmentation masks to estimate left ventricular volume and forms a final ejection fraction prediction by identifying cardiac cycles within an echocardiogram clip. The proposed PDA classification model implements a simple neural network trained on features extracted from PDA shunt masks. Results for the ventricular estimation task were found to be comparable to state-of the-art frameworks with mean absolute error of 6.6%. For the PDA classification task the proposed framework achieved an accuracy of 75% on a novel test set of colour echocardiogram videos.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".