Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging

Haq, Ikramul; Haq, Iqraa; Xu, Bo

doi:10.21037/cdt.2020.03.09

Cited by 26 publications

(25 citation statements)

References 54 publications

(84 reference statements)

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“…AI is a rapidly evolving transdisciplinary field which integrates computer science, statistics, psychology, neuroscience, material science, mechanical engineering and computer hardware design to develop algorithms that aim to simulate human intuition, decision-making and object recognition. 3 The overarching aims of AI in cardiovascular medicine are threefold: to optimize patient care, improve efficiency and improve clinical outcomes. 3 In cardiology, there has been a growth in the potential sources of new patient data, as well as advances in investigations and therapies, which position the field well to uniquely benefit from AI.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Haq¹,

Chhatwal²,

Sanaka³

et al. 2022

VHRM

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Cardiovascular disease (CVD) represents a significant and increasing burden on healthcare systems. Artificial intelligence (AI) is a rapidly evolving transdisciplinary field employing machine learning (ML) techniques, which aim to simulate human intuition to offer cost-effective and scalable solutions to better manage CVD. ML algorithms are increasingly being developed and applied in various facets of cardiovascular medicine, including and not limited to heart failure, electrophysiology, valvular heart disease and coronary artery disease. Within heart failure, AI algorithms can augment diagnostic capabilities and clinical decision-making through automated cardiac measurements. Occult cardiac disease is increasingly being identified using ML from diagnostic data. Improved diagnostic and prognostic capabilities using ML algorithms are enhancing clinical care of patients with valvular heart disease and coronary artery disease. The growth of AI techniques is not without inherent challenges, most important of which is the need for greater external validation through multicenter, prospective clinical trials.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Haq¹,

Chhatwal²,

Sanaka³

et al. 2022

VHRM

Self Cite

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“…Radiology is one of the most promising fields of radiomics and ML application, as these may be used for automatic detection and characterization of lesions or segmentation of medical images [12,13]. In particular, there has been a growing number of scientific works showing ML as a powerful tool in imaging of cardiovascular diseases [14]. For instance, it may improve image acquisition and reconstruction time [15].…”

Section: Introductionmentioning

confidence: 99%

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Spadarella

Perillo

Ugga

et al. 2022

Curr Cardiovasc Imaging Rep

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Purpose of Review This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging. Recent Findings Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases. Summary The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

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“…Significant advancements have also been reported in the generation of patient-specific models of the mitral valve from medical images (Zhang et al, 2017). Several review articles have been published in recent years (Haq et al, 2020;Mathur et al, 2020), identifying challenges that need to be addressed to further increase the realworld adoption of AI based applications in the diagnosis and treatment planning of CVD. In the following the focus is on two main challenges.…”

Section: Introductionmentioning

confidence: 99%

“…To develop robust algorithms, the databases used for training, validation and evaluation should cover the entire spectrum of pathological variations and combinations. If training datasets lack diversity, algorithms may be biased or skewed to certain types of patients (Haq et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-Preserving and Explainable AI for Cardiovascular Imaging

Puiu¹,

Vizitiu²,

Nita³

et al. 2021

STUD INFORM CONTROL

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Medical imaging provides valuable input for managing cardiovascular disease (CVD), ranging from risk assessment to diagnosis, therapy planning and follow-up. Artificial intelligence (AI) based medical image analysis algorithms provide nowadays state-of-the-art results in CVD management, mainly due to the increase in computational power and data storage capacities. Various challenges remain to be addressed to speed-up the adoption of AI based solutions in routine CVD management. Although medical imaging and in general health data are abundant, the access and transfer of such data is difficult to realize due to ethical considerations. Hence, AI algorithms are often trained on relatively small datasets, thus limiting their robustness, and potentially leading to biased or skewed results for certain patient or pathology sub-groups. Furthermore, explainability and interpretability have become core requirements for AI algorithms, to ensure that the rationale behind output inference can be revealed. The paper focuses on recent developments related to these two challenges, discusses the clinical impact of proposed solutions, and provides conclusions for further research and development. It also presents examples related to the diagnosis of stable coronary artery disease, a whole-body circulation model for the assessment of structural heart disease, and to the diagnosis and treatment planning of aortic coarctation, a congenital heart disease.

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Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging

Cited by 26 publications

References 54 publications

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Artificial Intelligence in Cardiovascular Medicine: Current Insights and Future Prospects

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Privacy-Preserving and Explainable AI for Cardiovascular Imaging

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