Comparing innovative artificial intelligence algorithms to assess echocardiographic videos for clinical modeling
Bibliographic record
Abstract
OBJECTIVE: To use multiple dynamic deep learning algorithms to develop predictive models with video-based echocardiographic images using sample size determination as a key variable to assess optimal performance metrics. METHODS: Our study compares performance of 3-dimensional convolutional neural networks, video vision transformers, and hybrid convolutional neural networks and long short-term memory models within both supervised learning and semi-supervised learning (SSL) domains using variable sample sizes. RESULTS: For supervised learning, the ResNet3D model achieved the lowest mean absolute error (MAE) and root mean squared error (RMSE) across all training set sizes (200-, 400-, and 800-video datasets), with the best performance observed on the 800-video training set (MAE = 7.409, RMSE = 10.216). In the SSL setting, both the ResNet3D and ResNet+LSTM models benefited from the inclusion of unlabeled data, particularly with larger data sets. CONCLUSIONS: Because SSL models use both labeled and unlabeled data sets, our findings are significant in showing that performance of certain predictive models using mixtures of unlabeled and labeled data is comparable to that of models using only labeled data with similar sample sizes, thus obviating the need for large sample sizes of labeled data.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".