Key result
DeepPhase CNN model achieves 0.96 AUC for automated cardiac phase detection.
Why the study?
Manual detection of end-systolic and end-diastolic phases during echocardiography is time-consuming and error-prone, creating a need for an automated technique to detect cardiac phases accurately.
Does the DeepPhase CNN model accurately automate the detection of cardiac phases in echocardiography compared to existing deep learning techniques?
Does the DeepPhase CNN model accurately automate the detection of cardiac phases in echocardiography compared to existing deep learning techniques?
The DeepPhase CNN model accurately automates the detection of end-systolic and end-diastolic phases in echocardiography without requiring left ventricle segmentation, potentially reducing manual interpretation time and errors.
Answers are grounded in this paper and cite their sources.
May streamline echocardiography workflows; extends deep learning approaches but leaves open prospective clinical validation.
Echocardiography is a commonly used and cost-effective test to assess heart conditions. During the test, cardiologists and technicians observe two cardiac phases-end-systolic (ES) and end-diastolic (ED)-which are critical for calculating heart chamber size and ejection fraction. However, non-essential frames called Non-ESED frames may appear between these phases. Currently, technicians or cardiologists manually detect these phases, which is time-consuming and prone to errors. To address this, an automated and efficient technique is needed to accurately detect cardiac phases and minimize diagnostic errors. In this paper, we propose a deep learning model called DeepPhase to assist cardiology personnel. Our convolutional neural network (CNN) learns from echocardiography images to identify the ES, ED, and Non-ESED phases without the need for left ventricle segmentation or electrocardiograms. We evaluate our model on three echocardiography image datasets, including the CAMUS dataset, the EchoNet Dynamic dataset, and a new dataset we collected from a cardiac hospital (CardiacPhase). Our model outperforms existing techniques, achieving 0.96 and 0.82 area under the curve (AUC) on the CAMUS and CardiacPhase datasets, respectively. We also propose a novel cropping technique to enhance the model's performance and ensure its relevance to real-world scenarios for ES, ED, and Non ES-ED classification.
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Farhad et al. (2023) studied Echocardiography cardiac phase detection (n=635). DeepPhase (Convolutional Neural Network) vs. Other CNN models (Siddiqi's CNN, VGG16, ResNet, EfficientNet, MobileNet, Lane et al., Wegner et al.) was evaluated on Area under the curve (AUC) for cardiac phase classification. The DeepPhase convolutional neural network model achieved an area under the curve of 0.96 on the CAMUS dataset and 0.82 on the CardiacPhase dataset for automated cardiac phase detection.
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