Deep‐learning based multibeat echocardiographic cardiac phase detection.
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| Title: | Deep‐learning based multibeat echocardiographic cardiac phase detection. |
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| Authors: | Cheng, Hanlin1 (AUTHOR), Shi, Zhongqing2,3,4 (AUTHOR), Qi, Zhanru2,3,4 (AUTHOR), Wang, Xiaoxian2,3,4 (AUTHOR), Guo, Guanjun2,3,4 (AUTHOR), Fang, Aijuan2,3,4 (AUTHOR), Jin, Zhibin2,3,4 (AUTHOR), Shan, Chunjie1 (AUTHOR), Du, Yue5 (AUTHOR), Chen, Ruiyang1 (AUTHOR), Qian, Sunnan6 (AUTHOR), Luo, Shouhua1 (AUTHOR) luoshouhua@seu.edu.cn, Yao, Jing2,3,4 (AUTHOR) jingyao@nju.edu.cn |
| Source: | Medical Physics. Jun2025, Vol. 52 Issue 6, p4278-4298. 21p. |
| Subjects: | Deep learning, Echocardiography, Signal detection, Classification algorithms, Clinical medicine, Heart beat |
| Abstract: | Background: End‐to‐end automatic detection of cardiac phase in multibeat echocardiograms is crucial for measuring cardiac parameters in clinical scenarios. However, existing studies face limitations due to the high cost of data annotation and collection, and time‐consuming detection processes. Purpose: This study introduces a novel multibeat echocardiographic cardiac phase detection network, EchoPhaseNet, to perform fast and accurate cardiac phase detection of variable‐length multibeat echocardiographic sequences, with low annotation costs and limited data. Materials and methods: Five echocardiographic datasets were used in this study, including a small‐scale private dataset, Echo‐DT (DrumTower), a medium‐scale publicly available dataset, PhaseDetection, and three additional publicly available datasets: EchoNet‐Dynamic, CAMUS, and EchoNet‐Dynamic‐MultiBeat. EchoPhaseNet and four other deep learning‐based cardiac phase detection methods were trained and internally validated on the Echo‐DT and PhaseDetection datasets (with sample ratios for training, validation, and testing set at 60%:20%:20% and 80%:0%:20%, respectively), and then externally validated on the other three datasets. Model performance was evaluated using GradCAM for qualitative visualization and absolute frame difference (aFD) for quantitative accuracy, with statistical significance assessed using Tukey's test and Benjamini–Hochberg correction, considering corrected p‐values <$<$ 0.05 as significant. Results: The annotation costs and accuracy of end‐diastolic (ED) and end‐systolic (ES) detection using EchoPhaseNet were compared with those of four other comparison methods. EchoPhaseNet achieves effective specific phase detections using only ED/ES labels, reducing annotation costs and making it applicable to a wider range of detection scenarios compared to all the comparison methods. On the Echo‐DT dataset, EchoPhaseNet's mean aFD values for ED and ES detection in the A4C view samples were 1.08 and 1.04, respectively, significantly outperforming three comparison methods in ED detection accuracy (p‐values <$<$ 0.01) and comparable to the remaining one (p‐values >$>$ 0.05). On the PhaseDetection dataset, EchoPhaseNet's mean aFD values for ED and ES detection were 1.67 and 2.19, respectively, comparable to the detection accuracies of all four comparison methods (p‐values >$>$ 0.05). In addition, EchoPhaseNet showed strong generalization ability on multiple external validation datasets. After training on the small‐scale Echo‐DT dataset, EchoPhaseNet significantly outperformed the four comparison methods (p‐values <$<$ 0.01) in ED detection, achieving mean aFD values of 1.67 and 1.11 on the EchoNet‐Dynamic and EchoNet‐Dynamic‐MultiBeat datasets, respectively. After training on the PhaseDetection dataset, EchoPhaseNet significantly outperformed the four compared methods (p‐values <$<$ 0.01) in ES detection on the EchoNet‐Dynamic dataset, achieving mean aFD value of 2.58. EchoPhaseNet's inference time for a single 32‐frame sequence segment is substantially lower than that of the four compared methods, not exceeding 8 ms on an RTX 4080 GPU using the PyTorch deep learning framework. Conclusions: EchoPhaseNet exhibits clear advantages over existing studies in data annotation and collection costs, as well as detection speed, and is applicable to a wider range of detection scenarios. It demonstrates good practicality and promising prospects for clinical multibeat echocardiographic cardiac phase detection. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Background: End‐to‐end automatic detection of cardiac phase in multibeat echocardiograms is crucial for measuring cardiac parameters in clinical scenarios. However, existing studies face limitations due to the high cost of data annotation and collection, and time‐consuming detection processes. Purpose: This study introduces a novel multibeat echocardiographic cardiac phase detection network, EchoPhaseNet, to perform fast and accurate cardiac phase detection of variable‐length multibeat echocardiographic sequences, with low annotation costs and limited data. Materials and methods: Five echocardiographic datasets were used in this study, including a small‐scale private dataset, Echo‐DT (DrumTower), a medium‐scale publicly available dataset, PhaseDetection, and three additional publicly available datasets: EchoNet‐Dynamic, CAMUS, and EchoNet‐Dynamic‐MultiBeat. EchoPhaseNet and four other deep learning‐based cardiac phase detection methods were trained and internally validated on the Echo‐DT and PhaseDetection datasets (with sample ratios for training, validation, and testing set at 60%:20%:20% and 80%:0%:20%, respectively), and then externally validated on the other three datasets. Model performance was evaluated using GradCAM for qualitative visualization and absolute frame difference (aFD) for quantitative accuracy, with statistical significance assessed using Tukey's test and Benjamini–Hochberg correction, considering corrected p‐values <$<$ 0.05 as significant. Results: The annotation costs and accuracy of end‐diastolic (ED) and end‐systolic (ES) detection using EchoPhaseNet were compared with those of four other comparison methods. EchoPhaseNet achieves effective specific phase detections using only ED/ES labels, reducing annotation costs and making it applicable to a wider range of detection scenarios compared to all the comparison methods. On the Echo‐DT dataset, EchoPhaseNet's mean aFD values for ED and ES detection in the A4C view samples were 1.08 and 1.04, respectively, significantly outperforming three comparison methods in ED detection accuracy (p‐values <$<$ 0.01) and comparable to the remaining one (p‐values >$>$ 0.05). On the PhaseDetection dataset, EchoPhaseNet's mean aFD values for ED and ES detection were 1.67 and 2.19, respectively, comparable to the detection accuracies of all four comparison methods (p‐values >$>$ 0.05). In addition, EchoPhaseNet showed strong generalization ability on multiple external validation datasets. After training on the small‐scale Echo‐DT dataset, EchoPhaseNet significantly outperformed the four comparison methods (p‐values <$<$ 0.01) in ED detection, achieving mean aFD values of 1.67 and 1.11 on the EchoNet‐Dynamic and EchoNet‐Dynamic‐MultiBeat datasets, respectively. After training on the PhaseDetection dataset, EchoPhaseNet significantly outperformed the four compared methods (p‐values <$<$ 0.01) in ES detection on the EchoNet‐Dynamic dataset, achieving mean aFD value of 2.58. EchoPhaseNet's inference time for a single 32‐frame sequence segment is substantially lower than that of the four compared methods, not exceeding 8 ms on an RTX 4080 GPU using the PyTorch deep learning framework. Conclusions: EchoPhaseNet exhibits clear advantages over existing studies in data annotation and collection costs, as well as detection speed, and is applicable to a wider range of detection scenarios. It demonstrates good practicality and promising prospects for clinical multibeat echocardiographic cardiac phase detection. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00942405 |
| DOI: | 10.1002/mp.17733 |
