Enhancing Diagnostic Accuracy for Medical Imaging and Radiology with AI-Driven Synergy Tools: Enabling Early Intervention and Preventive Measures through Early Detection of Cardiovascular Conditions
Keywords:
Artificial Intelligence, Medical Imaging, Radiology, Diagnostic Accuracy, Cardiovascular Conditions, AI-Driven Synergy Tools
Abstract
The integration of artificial intelligence (AI) into medical imaging and radiology has markedly enhanced diagnostic accuracy, enabling early intervention and preventive measures for cardiovascular conditions. AI-powered synergy tools, such as deep learning algorithms and convolutional neural networks (CNNs), analyze medical images with high precision, facilitating the early detection of abnormalities. This comprehensive article explores the advancements in AI technologies in medical imaging, focusing on their impact on cardiovascular diagnostics. We delve into the methodologies employed in AI research, review relevant literature, present findings from recent studies, and discuss the implications for clinical practice. The discussion underscores the potential of AI to revolutionize radiology, improve patient outcomes, and reduce healthcare costs by preventing the progression of cardiovascular diseases through timely intervention.
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Abbasi, N. (2024). Artificial Intelligence in Remote Monitoring and Telemedicine. Journal of Artificial Intelligence General Science (JAIGS), 1(1), 258–272. https://doi.org/10.60087/jaigs.v1i1.202
Bustos, A., Pertusa, A., Salinas, J., & De La Iglesia-Vayá, M. (2020). PadChest: A large chest x-ray image dataset with multi-label annotated reports. Medical Image Analysis, 66, 101797. https://doi.org/10.1016/j.media.2020.101797
Chen, J. H., & Asch, S. M. (2017). Machine learning and prediction in medicine — beyond the peak of inflated expectations. New England Journal of Medicine, 376(26), 2507–2509. https://doi.org/10.1056/nejmp1702071
Doshi-Velez, F., Kortz, M., Budish, R., Bavitz, C., Gershman, S., O’Brien, D., Scott, K., Schieber, S., Waldo, J., Weinberger, D., Weller, A., & Wood, A. (2017, November 3). Accountability of AI under the Law: The role of explanation. arXiv.org. https://arxiv.org/abs/1711.01134
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Litjens, G. J. S., Kooi, T., Bejnordi, B. E., Setio, A. a. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. a. W. M., Van Ginneken, B., & Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60–88. https://doi.org/10.1016/j.media.2017.07.005
McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., . . . Shetty, S. (2020). International evaluation of an AI system for breast cancer screening. Nature, 577(7788), 89–94. https://doi.org/10.1038/s41586-019-1799-6
Narula, S., Shameer, K., Omar, A. M. S., Dudley, J. T., & Sengupta, P. P. (2016). Machine-Learning algorithms to automate morphological and functional assessments in 2D echocardiography. Journal of the American College of Cardiology, 68(21), 2287–2295. https://doi.org/10.1016/j.jacc.2016.08.062
Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future — big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216–1219. https://doi.org/10.1056/nejmp1606181
Rubin, D. L. (2019). Artificial intelligence in Imaging: The Radiologist’s role. Journal of the American College of Radiology, 16(9), 1309–1317. https://doi.org/10.1016/j.jacr.2019.05.036
Schwarz, K., Liao, Y., & Geiger, A. (2021, November 3). On the Frequency Bias of Generative Models. arXiv.org. https://arxiv.org/abs/2111.02447
Shortliffe, E. H., & Sepúlveda, M. J. (2015, May 20). Clinical decision support in the era of artificial intelligence. PSNet. https://psnet.ahrq.gov/issue/clinical-decision-support-era-artificial-intelligence
Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019, May 13). What clinicians want: Contextualizing explainable machine learning for clinical end use. arXiv.org. https://arxiv.org/abs/1905.05134
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Wang, J., Xie, G., Huang, Y., Lyu, J., Zheng, Y., Zheng, F., & Jin, Y. (2022, January 22). FEDMeD-GAN: Federated Domain Translation on Unsupervised Cross-Modality Brain Image Synthesis. arXiv.org. https://arxiv.org/abs/2201.08953
Zhang, J., Gajjala, S., Agrawal, P., Tison, G. H., Hallock, L. A., Beussink-Nelson, L., Lassen, M. H., Fan, E., Aras, M. A., Jordan, C., Fleischmann, K. E., Melisko, M., Qasim, A., Shah, S. J., Bajcsy, R., & Deo, R. C. (2018). Fully automated echocardiogram interpretation in clinical practice. Circulation, 138(16), 1623–1635. https://doi.org/10.1161/circulationaha.118.034338
Published
2024-08-23
How to Cite
Zeb, S., Fnu, N., Fahad, M., Qayyum, M. U., & Abbasi, N. (2024). Enhancing Diagnostic Accuracy for Medical Imaging and Radiology with AI-Driven Synergy Tools: Enabling Early Intervention and Preventive Measures through Early Detection of Cardiovascular Conditions. European Journal of Science, Innovation and Technology, 4(4), 38-47. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/483
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Copyright (c) 2024 Shah Zeb, Nizamullah Fnu, Muhammad Fahad, Muhammad Umer Qayyum, Nasrullah Abbasi
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