Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation

Dong Ok Kim (1), Minsu Chae (2), HwaMin Lee (3)
(1) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, 02708, Republic of Korea
(2) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, 02708, Republic of Korea
(3) Department of Biomedical Informatics, Korea University College of Medicine, Seoul, 02708, Republic of Korea
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How to cite (IJASEIT) :
Kim, Dong Ok, et al. “Revolutionizing Echocardiography: A Comparative Study of Advanced AI Models for Precise Left Ventricular Segmentation”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 3, June 2024, pp. 835-40, doi:10.18517/ijaseit.14.3.18073.
Cardiovascular diseases, a leading cause of global mortality, underscore the urgency for refined diagnostic techniques. Among these, cardiomyopathies characterized by abnormal heart wall thickening present a formidable challenge, exacerbated by aging populations and the side effects of chemotherapy. Traditional echocardiogram analysis, demanding considerable time and expertise, now faces overwhelming pressure due to escalating demands for cardiac care. This study addresses these challenges by harnessing the potential of Convolutional Neural Networks, specifically YOLOv8, U-Net, and Attention U-Net, leveraging the EchoNet-Dynamic dataset from Stanford University Hospital to segment echocardiographic images. Our investigation aimed to optimize and compare these models for segmenting the left ventricle in echocardiography images, a crucial step for quantifying key cardiac parameters. We demonstrate the superiority of U-Net and Attention U-Net over YOLOv8, with Attention U-Net achieving the highest Dice Coefficient Score due to its focus on relevant features via attention mechanisms. This finding highlights the importance of model specificity in medical image segmentation and points to attention mechanisms. The integration of AI in echocardiography represents a pivotal shift toward precision medicine, improving diagnostic accuracy and operational efficiency. Our results advocate for the continued development and application of AI-driven models, underscoring their potential to transform cardiovascular diagnostics through enhanced precision and multimodal data integration. This study validates the effectiveness of state-of-the-art AI models in cardiac function assessment and paves the way for their implementation in clinical settings, thereby contributing significantly to the advancement of cardiac healthcare delivery.

C. A. Roberts, M. Binder, and D. Antoine, "Reflections on Cardiovascular Disease," The Bioarchaeology of Cardiovascular Disease, pp. 258-262, 2023.

C. McAloon, F. Osman, P. Glennon, P. Lim, and S. Hayat, "Global epidemiology and incidence of cardiovascular disease," Cardiovascular Diseases, pp. 57-96: Elsevier, 2016.

A. Džubur, E. Begić, A. Durak-Nalbantić, and B. Aziri, “Cardiomyopathies,” Galenika Medical Journal, vol. 2, no. 5, pp. 23-30, 2023.

I. Kåks, M. Leopoulou, G. Mattsson, and P. Magnusson, “An Overview of the Cardiomyopathies,” Cardiomyopathy-Disease of the Heart Muscle, 2021.

K. A. Sytdykova, Y. V. Kazantseva, A. M. Sinanyan, M. N. Dulkina, G. O. Fatenkov, and M. A. Fatenkova, “Age-related risk factors affecting the population morbidity,” Cardiometry, no. 27, pp. 221-228, 2023.

K. England, and N. Azzopardi-Muscat, “Demographic trends and public health in Europe,” The European Journal of Public Health, vol. 27, no. suppl_4, pp. 9-13, 2017.

K. Fendrich, N. van den Berg, U. Siewert, and W. Hoffmann, “Demographic change: Demands on the health care system and solutions using the example of Mecklenburg–Western Pomerania,” Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz, vol. 53, pp. 479-485, 2010.

R. M. Lang, L. P. Badano, V. Mor-Avi, J. Afilalo, A. Armstrong, L. Ernande, F. A. Flachskampf, E. Foster, S. A. Goldstein, and T. Kuznetsova, “Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging,” European Heart Journal-Cardiovascular Imaging, vol. 16, no. 3, pp. 233-271, 2015.

H. Cui, L. Hu, and L. Chi, “Advances in computer-aided medical image processing,” Applied Sciences, vol. 13, no. 12, pp. 7079, 2023.

B. S. Chandra, and S. Jana, “Towards Next-Generation Cardiac Care in Resource-Constrained and High-Risk Scenarios,” Indian institute of technology Hyderabad, 2019.

H. Salah, “Improving cardiac care delivery using predictive and prescriptive analytics,” University of Missouri--Columbia, 2022.

G. R. Djavanshir, X. Chen, and W. Yang, “A Review of artificial intelligence's neural networks (deep learning) applications in medical diagnosis and prediction,” It Professional, vol. 23, no. 3, pp. 58-62, 2021.

D. Ouyang, B. He, A. Ghorbani, N. Yuan, J. Ebinger, C. P. Langlotz, P. A. Heidenreich, R. A. Harrington, D. H. Liang, and E. A. Ashley, “Video-based AI for beat-to-beat assessment of cardiac function,” Nature, vol. 580, no. 7802, pp. 252-256, 2020.

S. Zheng, X. Ye, J. Tan, Y. Yang, and L. Li, "Dual-attention deep fusion network for multi-modal medical image segmentation." pp. 577-587.

Y. Wang, C. Yin, and P. Zhang, “Multimodal Risk Prediction with Physiological Signals, Medical Images and Clinical Notes,” Heliyon, 2023.

T. K. M. Wang, and A. L. Klein, “Multi-Modality Cardiac Imaging for Pericardial Diseases: A Contemporary Review,” Reviews in Cardiovascular Medicine, vol. 23, no. 10, pp. 336, 2022.

M. Wamil, M. Goncalves, A. Rutherford, A. Borlotti, and P. A. Pellikka, “Multi-modality cardiac imaging in the management of diabetic heart disease,” Frontiers in Cardiovascular Medicine, vol. 9, pp. 1043711, 2022.

N. Sriraam, T. Sushma, and S. Suresh, “A Computer-Aided Markov Random Field Segmentation Algorithm for Assessing Fetal Ventricular Chambers,” Critical Reviews™ in Biomedical Engineering, vol. 51, no. 1, 2023.

N. Joshi, and S. Jain, “Improved Segmentation of Cardiac MRI Using Efficient Pre-Processing Techniques,” Journal of Information Technology Research (JITR), vol. 15, no. 1, pp. 1-14, 2022.

É. O. Rodrigues, F. F. C. de Morais, and A. Conci, “On the automated segmentation of epicardial and mediastinal cardiac adipose tissues using classification algorithms,” arXiv preprint arXiv:2208.14352, 2022.

M. Penso, M. Babbaro, S. Moccia, M. Guglielmo, M. L. Carerj, C. M. Giacari, M. Chiesa, R. Maragna, M. G. Rabbat, and A. Barison, “Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment,” Journal of Cardiovascular Magnetic Resonance, vol. 24, no. 1, pp. 62, 2022.

S. P. Primakov, A. Ibrahim, J. E. van Timmeren, G. Wu, S. A. Keek, M. Beuque, R. W. Granzier, E. Lavrova, M. Scrivener, and S. Sanduleanu, “Automated detection and segmentation of non-small cell lung cancer computed tomography images,” Nature communications, vol. 13, no. 1, pp. 3423, 2022.

L. Glenn Jocher, Ayush Chaurasia. "Instance Segmentation,"

M. Bal-Ghaoui, M. H. El Yousfi Alaoui, A. Jilbab, and A. Bourouhou, “U-Net transfer learning backbones for lesions segmentation in breast ultrasound images,” International Journal of Electrical & Computer Engineering (2088-8708), vol. 13, no. 5, 2023.

O. Ronneberger, P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation." pp. 234-241.

A. M. H. Mahran, W. Hussein, and S. E. D. M. Saber, “Automatic Teeth Segmentation Using Attention U-Net,” 2023.

O. Oktay, J. Schlemper, L. L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N. Y. Hammerla, and B. Kainz, “Attention u-net: Learning where to look for the pancreas,” arXiv preprint arXiv:1804.03999, 2018.

R. Azad, E. K. Aghdam, A. Rauland, Y. Jia, A. H. Avval, A. Bozorgpour, S. Karimijafarbigloo, J. P. Cohen, E. Adeli, and D. Merhof, “Medical image segmentation review: The success of u-net,” arXiv preprint arXiv:2211.14830, 2022.

J. Du, "Understanding of object detection based on CNN family and YOLO." p. 012029.

M. Melinščak, "Attention-based U-net: Joint Segmentation of Layers and Fluids from Retinal OCT Images." pp. 391-396.

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