Magnetic resonance imaging

Ferreira, Vanessa M.; Robson, Matthew D; Karamitsos, Theodoros D.; Bissell, Malenka M.; Tyler, Damian J; Neubauer, Stefan

doi:10.1016/b978-1-78242-282-2.00006-8

Cited by 4 publications

References 180 publications

(147 reference statements)

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Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

et al. 2019

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Healthcare, conceivably more than any other area of human endeavour, has the greatest potential to be affected by artificial intelligence (AI). This potential has been shown by several reports that demonstrate equal or superhuman performance in medical tasks that aim to improve efficiency, diagnosis and prognosis. This review focuses on the state of the art of AI applications in cardiovascular imaging. It provides an overview of the current applications and studies performed, including the potential value, implications, limitations and future directions of AI in cardiovascular imaging.

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Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

et al. 2019

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Biological and Medical Aspects of Electromagnetic Fields

2018

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Relevance of Machine Learning to Cardiovascular Imaging

Sasidharan

Salmasi

Pirola

et al. 2022

Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention

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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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Magnetic resonance imaging

Cited by 4 publications

References 180 publications

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist

Biological and Medical Aspects of Electromagnetic Fields

Relevance of Machine Learning to Cardiovascular Imaging

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