Utilizing Weighted Spatio-Temporal Information for Automated Transthoracic Echocardiography View Classification
Bibliographic record
Abstract
Transthoracic echocardiography (TTE) is a critical imaging modality for assessing cardiac function and diagnosing cardiomyopathies. These videos, captured from various heart angles, must be accurately categorized for comprehensive diagnosis, making automated view classification essential. This study develops and evaluates multiple model architectures and feature fusion techniques to classify eleven standard TTE views from video input. We examine both spatial and temporal feature extraction approaches alongside a Dynamic Feature Fusion technique (DFF) that adaptively balances the importance of either feature in analyzing TTE studies. Our results highlight the superior performance of a model architecture utilizing an EfficientNet and Dilated Convolutional Networks, achieving a micro F1 Score of 0.951. Additionally, we leverage a custom cycle detector algorithm to partition a TTE sequence into individual cardiac cycles, i.e., heartbeats, providing consistent input for model training and enabling an ensemble technique for a more reliable prediction. Our findings present valuable insights into effective TTE analysis, identifying the most effective model architecture for echocardiogram interpretation and advancing clinical workflows.Clinical RelevanceThis approach improves echocardiogram view classification, aiding clinicians by streamlining work-flows and reducing errors. The method can also be applied to broader TTE data analysis tasks, such as disease classification, while offering insights into interpretable spatial and temporal patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".