Machine learning in cardiac CT: Basic concepts and contemporary data

Singh, Gurpreet; Al’Aref, Subhi J.; Assen, Marly van; Kim, Timothy Suyong; Rosendael, Alexander van; Kolli, Kranthi K.; Dwivedi, Aeshita; Maliakal, Gabriel; Pandey, Mohit; Wang, Jing; Do, Virginie; Gummalla, Manasa; Cecco, Carlo N. De; Min, James K.

doi:10.1016/j.jcct.2018.04.010

Cited by 95 publications

(54 citation statements)

References 70 publications

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“…Another interesting potential application of AI techniques could be in cardiac CT, for patients with suspected CAD. For patients suffering from these conditions, the association between cardiac CT and ML algorithms has shown a potential in clinical practice to take noninvasive approaches and to detect functional information beyond atherosclerotic plaque characterization [ 16 – 18 ].…”

Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Romiti

Vinciguerra

Saade

et al. 2020

Cardiology Research and Practice

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Cardiovascular disease (CVD), despite the significant advances in the diagnosis and treatments, still represents the leading cause of morbidity and mortality worldwide. In order to improve and optimize CVD outcomes, artificial intelligence techniques have the potential to radically change the way we practice cardiology, especially in imaging, offering us novel tools to interpret data and make clinical decisions. AI techniques such as machine learning and deep learning can also improve medical knowledge due to the increase of the volume and complexity of the data, unlocking clinically relevant information. Likewise, the use of emerging communication and information technologies is becoming pivotal to create a pervasive healthcare service through which elderly and chronic disease patients can receive medical care at their home, reducing hospitalizations and improving quality of life. The aim of this review is to describe the contemporary state of artificial intelligence and digital health applied to cardiovascular medicine as well as to provide physicians with their potential not only in cardiac imaging but most of all in clinical practice.

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Section: What Is Artificial Intelligence?mentioning

confidence: 99%

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Romiti

Vinciguerra

Saade

et al. 2020

Cardiology Research and Practice

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“…61 ML and its applications to CTCA has been previously very well reviewed. 62 The integration of ML into clinical practice will bring exciting and adding it to stenosis severity. 64 ML-based fractional flow reserve has also been shown to perform well at predicting ischaemic lesions.…”

Section: Machine Learningmentioning

confidence: 99%

Latest Advances in Cardiac CT

et al. 2020

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Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

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“…[19][20][21] Machine learning has since been applied to various tasks in cardiovascular and thoracic imaging including segmentation, characterization, quantification, lung nodule detection and measurement, and lung cancer prognosis and treatment. Many of these applications have been consolidated in prior reviews, [22][23][24] and we provide a survey of those relevant to Architecture of a cascaded system of multiple neural networks, each building upon the outputs of a preceding network. In this example, the (a) proposal network identifies candidate pulmonary nodules, the (b) classification network identifies "true" pulmonary nodules from false positives, and the (c) segmentation network delineates the boundaries of each nodule.…”

Section: Applications In Cardiovascular and Thoracic Imagingmentioning

confidence: 99%

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

Retson

Besser

Sall³

et al. 2019

Journal of Thoracic Imaging

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Advances in technology have always had the potential and opportunity to shape the practice of medicine, and in no medical specialty has technology been more rapidly embraced and adopted than radiology. Machine learning and deep neural networks promise to transform the practice of medicine, and, in particular, the practice of diagnostic radiology. These technologies are evolving at a rapid pace due to innovations in computational hardware and novel neural network architectures. Several cutting-edge postprocessing analysis applications are actively being developed in the fields of thoracic and cardiovascular imaging, including applications for lesion detection and characterization, lung parenchymal characterization, coronary artery assessment, cardiac volumetry and function, and anatomic localization. Cardiothoracic and cardiovascular imaging lies at the technological forefront of radiology due to a confluence of technical advances. Enhanced equipment has enabled computed tomography and magnetic resonance imaging scanners that can safely capture images that freeze the motion of the heart to exquisitely delineate fine anatomic structures. Computing hardware developments have enabled an explosion in computational capabilities and in data storage. Progress in software and fluid mechanical models is enabling complex 3D and 4D reconstructions to not only visualize and assess the dynamic motion of the heart, but also quantify its blood flow and hemodynamics. And now, innovations in machine learning, particularly in the form of deep neural networks, are enabling us to leverage the increasingly massive data repositories that are prevalent in the field. Here, we discuss developments in machine learning techniques and deep neural networks to highlight their likely role in future radiologic practice, both in and outside of image interpretation and analysis. We discuss the concepts of validation, generalizability, and clinical utility, as they pertain to this and other new technologies, and we reflect upon the opportunities and challenges of bringing these into daily use.

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Machine learning in cardiac CT: Basic concepts and contemporary data

Cited by 95 publications

References 70 publications

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Artificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance

Latest Advances in Cardiac CT

Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging

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