Key result
A model architecture utilizing EfficientNet and Dilated Convolutional Networks achieved a micro F1 Score of 0.951 for classifying eleven standard transthoracic echocardiography views.
Why the study?
TTE videos captured from various angles must be accurately categorized for comprehensive diagnosis, making automated view classification essential.
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An automated machine learning model using EfficientNet and Dilated Convolutional Networks accurately classifies standard TTE views, potentially streamlining clinical workflows.
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.
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Ghods et al. (2025) studied Transthoracic echocardiography view classification. EfficientNet and Dilated Convolutional Networks with Dynamic Feature Fusion vs. Other model architectures was evaluated on Classification of eleven standard TTE views (micro F1 Score). A model architecture utilizing EfficientNet and Dilated Convolutional Networks achieved a micro F1 Score of 0.951 for classifying eleven standard transthoracic echocardiography views.
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