Key result
ResNet-RNN model identifies ED and ES cardiac phases within ~3 frames in A4C view.
Why the study?
Detecting end-systolic and end-diastolic frames in echocardiography sequences presents a challenge that is crucial for assessing cardiac function.
Does an integrated autoencoder and RNN model accurately detect end-systolic and end-diastolic frames in 2D echocardiography sequences?
Does an integrated autoencoder and RNN model accurately detect end-systolic and end-diastolic frames in 2D echocardiography sequences?
An integrated deep learning model combining autoencoders and RNNs can accurately identify end-systolic and end-diastolic frames in 2D echocardiography, facilitating automated cardiac function assessment.
Answers are grounded in this paper and cite their sources.
May enable automated echocardiography analysis; hypothesis-generating and requires prospective validation before clinical adoption.
This study addresses the challenge of detecting end-systolic (ES) and end-diastolic (ED) frames in echocardiography sequences, which is crucial for assessing cardiac function. We propose an integrated approach that combines a ResNet-based autoencoder for image feature extraction with recurrent neural networks (RNNs) for frame labeling. The autoencoder, optimized with Sobel and MSE loss functions, processes echocardiography sequences to reconstruct despeckled images. Based on transfer learning, the features from the autoencoder are then fed into an RNN model with Long Short-Term Memory (LSTM) and dense layers for frame label prediction. By using datasets including EchoNet, EchoNetpediatric, CAMUS, and PACS data, our model achieved an average absolute frame difference (aaFD) of 2.61 and 2.12 frames for ED and ES in apical four- chamber (A4C) view, and aaFD of 3.12 and 2.54 frames in apical two- chamber (A2C) view, respectively. This method demonstrates a promising step towards automated cardiac phase recognition in 2D echocardiography sequences.
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Akbari et al. (2024) studied Cardiac phase recognition in 2D echocardiography. Integrated ResNet-based autoencoder and RNN model was evaluated on Average absolute frame difference (aaFD) for end-diastolic (ED) and end-systolic (ES) frames. An integrated ResNet-based autoencoder and RNN model for automated cardiac phase recognition achieved an average absolute frame difference of 2.61 and 2.12 frames for ED and ES in A4C view.
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