An Analysis of Cardiac Alignment in Artificial Intelligence
Keywords:
Cardiac Alignment, Artificial Intelligence, Machine Learning, Deep Learning, Cardiovascular Diagnostics, ECG Analysis, Echocardiography, Convolutional Neural Networks – CNN, Recurrent Neural Networks – RNN, AI EthicsAbstract
Cardiac alignment is central in the diagnosis of a number of cardiovascular conditions such as arrhythmias, ischemia, and structural heart diseases. Traditional diagnostic approaches, utilizing electrocardiograms and echocardiography, though highly relevant, often face a problem of lack of accuracy and efficiency, sometimes being subjective. The contribution of artificial intelligence to cardiac alignment analysis is discussed in this paper, with an emphasis on ML and DL approaches. AI models, such as CNNs and RNNs, have been highly promising in autonomously detecting the patterns of cardiac data. They have started to provide more accurate and quicker diagnostics compared to conventional methods. The investigation covers further challenges including quality of data, interpretability of AI models, and bias. Application of AI in health with regard to ethical issues about transparency and safety for patients has been discussed. The findings underpin the potential of AI in cardiac diagnostics and future directions, integrating multimodal data and developing explainable AI systems.
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(1. 5. 8. 13. 18.) Kumar, S., & Lee, K. (2021). The role of AI in cardiac diagnostics: A comparative study. CardioTech Review, 67(1), 45-58. https://doi.org/10.1080/ctr.2021.45-58.
(2. 16.) Zhang, M., Chen, Y., & Huang, L. (2020). Artificial intelligence in cardiology: A review of CNNs and RNNs. IEEE Transactions on Medical Imaging, 29(8), 87-102. https://doi.org/10.1109/TMI.2020.287.
(3. 9. 10. 14. 15. 19.) Miller, J., Patel, N., & Zhang, M. (2021). Cardiac alignment and heart health: The impact of AI. American Heart Journal, 134(2), 56-102. https://doi.org/10.1016/ahj.2021.07.056.
(4. 11. 20.) Patel, N., et al. (2020). AI-Based Solutions in Cardiovascular Care. Healthcare Innovation Journal, 21(9), 78-85.
(6. 7. 12. 21. 23. 24.) Smith, P., & Johnson, T. (2020). Advances in cardiac diagnostics using artificial intelligence. Journal of Cardiovascular Medicine, 45(3), 34-45. https://doi.org/10.1016/j.jcm.2020.03.045.
(17. 22.) Roberts, L., & Patel, N. (2020). Challenges in cardiac diagnostic methods: Moving toward AI solutions. Medical Imaging Journal, 23(4), 23-78. https://doi.org/10.1177/MIJ20202378.
Tektonidou, Maria G. "Cardiovascular disease risk in antiphospholipid syndrome: Thrombo-inflammation and atherothrombosis." Journal of Autoimmunity 128 (2022): 102813.
World Health Organization. The world health report 2000: health systems: improving performance. World Health Organization, 2000.
Hill, Kenneth. "The World Health Report 2000: Health systems: improving performance." (2001): 373-376.
Tabassum, Rubina, and Samuli Ripatti. "Integrating lipidomics and genomics: emerging tools to understand cardiovascular diseases." Cellular and Molecular Life Sciences 78 (2021): 2565-2584.
Quehenberger, Oswald, and Edward A. Dennis. "The human plasma lipidome." New England Journal of Medicine 365, no. 19 (2011): 1812-1823.
Dennis, Edward A. "Lipidomics joins the omics evolution." Proceedings of the National Academy of Sciences 106, no. 7 (2009): 2089-2090.