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Relevance of Machine Learning to Cardiovascular Imaging

Relevance of Machine Learning to Cardiovascular Imaging

Sumesh Sasidharan, M. Yousuf Salmasi, Selene Pirola, Omar A. Jarral
Copyright: © 2021 |Pages: 22
ISBN13: 9781799850717|ISBN10: 1799850714|EISBN13: 9781799850724
DOI: 10.4018/978-1-7998-5071-7.ch003
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MLA

Sasidharan, Sumesh, et al. "Relevance of Machine Learning to Cardiovascular Imaging." Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, IGI Global, 2021, pp. 78-99. https://doi.org/10.4018/978-1-7998-5071-7.ch003

APA

Sasidharan, S., Salmasi, M. Y., Pirola, S., & Jarral, O. A. (2021). Relevance of Machine Learning to Cardiovascular Imaging. In S. Saxena & S. Paul (Eds.), Deep Learning Applications in Medical Imaging (pp. 78-99). IGI Global. https://doi.org/10.4018/978-1-7998-5071-7.ch003

Chicago

Sasidharan, Sumesh, et al. "Relevance of Machine Learning to Cardiovascular Imaging." In Deep Learning Applications in Medical Imaging, edited by Sanjay Saxena and Sudip Paul, 78-99. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-5071-7.ch003

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Abstract

Artificial intelligence (AI) broadly concerns analytical algorithms that iteratively learn from big datasets, allowing computers to find concealed insights. These encompass a range of operations comprising several terms, including machine learning(ML), cognitive learning, deep learning, and reinforcement learning-based methods that can be used to incorporate and comprehend complex biomedical and healthcare data in scenarios where traditional statistical approaches cannot be implemented. For cardiovascular imaging in particular, machine learning guarantees to be a transformative tool that can address many unmet needs for patient-specific management, accurate prediction of disease progression, and the tracking of identifiable biomarkers of disease processes. In this chapter, the authors discuss fundamentals of machine learning algorithms for image analysis in the cardiovascular system by evaluating the need for ML in this field and examining the potential obstacles and challenges of implementation in the context of three common imaging modalities used in cardiovascular medicine.

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