Abstract
Echocardiography is a widely used, affordable, and high-throughput test to evaluate heart conditions. There are two cardiac phases known as end-systolic (ES) and end-diastolic (ED), which are observed by cardiologists and technicians during echocardiography. These phases are used to perform critical calculations during echocardiography, such as calculating heart chamber size and the calculation of ejection fraction. Typically, ED and ES phase detection is performed manually by a technician or cardiologist, which makes echocardiography interpretation time-consuming and error-prone. Therefore, it is crucial to develop an automated and efficient technique to detect the cardiac phases and minimize diagnostic errors. In this paper, we propose a deep learning model, namely DeepPhase, for accurate and automated interpretation of the ES and ED phases to assist cardiology personnel. Our proposed convolutional neural network (CNN) is designed to learn from echocardiography images and identify the cardiac phase from the given image without segmentation of left ventrical or the use of electrocardiograms. We have performed an extensive evaluation on two-real world echocardiography image datasets, namely, the benchmark Cardiac Acquisitions for Multistructure Ultrasound Segmentation (CAMUS) dataset and a new dataset (referred to as CardiacPhase) that we collected from a cardiac hospital. The proposed model outperformed relevant state-of-the-art techniques by achieving 0.98 and 0.92 area under the curve (AUC) on the CAMUS dataset, and the CardiacPhase dataset, respectively. We have proved the generalizability of our proposed work by training and testing with different cardiac views.
This work is funded in part by United Arab Emirates University collaborative team grant number 31R239.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdi, A.H., et al.: Automatic quality assessment of echocardiograms using convolutional neural networks: feasibility on the apical four-chamber view. IEEE TMI 36(6), 1221–1230 (2017)
Barcaro, U., Moroni, D., Salvetti, O.: Automatic computation of left ventricle ejection fraction from dynamic ultrasound images. Pattern Recogn. Image Anal. 18, 351–358 (2008)
Brownlee, J.: Better deep learning. Machine Learning Mastery (2020)
Darvishi, S., Behnam, H., Pouladian, M., Samiei, N.: Measuring left ventricular volumes in two-dimensional echocardiogra-phy image sequence using level-set method for automatic detection of end-diastole and end-systole frames. Cardiovasc. Med. 1, 39 (2012)
Ghorbani, A., et al.: Deep learning interpretation of echocardiograms. bioRxiv (2019)
Kusunose, K., Haga, A., Inoue, M., Fukuda, D., Yamada, H., Sata, M.: Clinically feasible and accurate view classification of echocardiographic images using deep learning. Biomolecules 10, 665–671 (2020)
Leclerc, S., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198–2210 (2019)
Madani, A., Ong, J.R., Tibrewal, A., Mofrad, M.R.K.: Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease. NPJ Dig. Med. 1(1), 21–30 (2018)
Moradi, S., et al.: MFP-UNET: a novel deep learning based approach for left ventricle SEG in echocardiography. Physica Medica 67, 58–69 (2019)
Nielsen, M.A.: Neural Networks and Deep Learning. Determination press, San Francisco (2015)
Rao, S., et al.: Physician burnout, engagement and career satisfaction in a large academic medical practice. Clin. Med. Res. 18, 3–10 (2020)
Siddiqi, R.: Automated pneumonia diagnosis using a customized sequential convolutional neural network. In: Proceedings of the 2019 3rd International Conference on Deep Learning Technologies, ICDLT 2019, pp. 64–70. ACM, New York (2019)
Sulas, E., Urru, M., Tumbarello, R., Raffo, L., Pani, D.: Automatic detection of complete and measurable cardiac cycles in antenatal pulsed-wave doppler signals. Comp. Methods Prog. Biomed. 190, 105336–105345 (2020)
Taheri Dezaki, F., et al.: Cardiac phase detection in echocardiograms with densely gated recurrent neural networks and global extrema loss. IEEE TMI 38(8), 1821–1832 (2019)
Yang, L., Zeng, S., Zhou, Y., Pan, B., Feng, Y., Li, D.: Design of convolutional neural network based on tree fork module. In: 2019 18th International Symposium on Distributed Computer and Applications for Business Engineering and Science (DCABES), pp. 1–4 (2019)
Acknowledgement
This work is funded in part by United Arab Emirates University collaborative team grant number 31R239.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Farhad, M., Masud, M.M., Beg, A. (2022). Deep Learning Based Cardiac Phase Detection Using Echocardiography Imaging. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13087. Springer, Cham. https://doi.org/10.1007/978-3-030-95405-5_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-95405-5_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95404-8
Online ISBN: 978-3-030-95405-5
eBook Packages: Computer ScienceComputer Science (R0)