Machine learning for nuclear cardiology: The way forward

Shrestha, Sirish; Sengupta, Partho P.

doi:10.1007/s12350-018-1284-x

Cited by 35 publications

(28 citation statements)

References 21 publications

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“…Datadriven approaches such as blind source separation techniques based on unsupervised neural networks have been extensively researched for the extraction of different physiological components (e.g., arterial input function for kinetic modeling) from dynamic PET scans [190][191][192][193]. In addition, various machine learning techniques have been applied to myocardial perfusion SPECT images for the identification of the perfusion defects and location, in addition to the improvement of the diagnostic and prognostic accuracies [194].…”

Section: Artificial Intelligence In Nuclear Medicinementioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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Attenuation correction (AC) is essential for the generation of artifact-free and quantitaAtively accurate positron emission tomography (PET) images. PET AC based on computed tomography (CT) frequently results in artifacts in attenuationcorrected PET images, and these artifacts mainly originate from CT artifacts and PET-CT mismatches. The AC in PET combined with a magnetic resonance imaging (MRI) scanner (PET/MRI) is more complex than PET/CT, given that MR images do not provide direct information on high energy photon attenuation. Deep learning (DL)-based methods for the improvement of PET AC have received significant research attention as alternatives to conventional AC methods. Many DL studies were focused on the transformation of MR images into synthetic pseudo-CT or attenuation maps. Alternative approaches that are not dependent on the anatomical images (CT or MRI) can overcome the limitations related to current CT-and MRI-based ACs and allow for more accurate PET quantification in stand-alone PET scanners for the realization of low radiation doses. In this article, a review is presented on the limitations of the PET AC in current dual-modality PET/CT and PET/MRI scanners, in addition to the current status and progress of DL-based approaches, for the realization of improved performance of PET AC.

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Section: Artificial Intelligence In Nuclear Medicinementioning

confidence: 99%

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

Lee

2021

IEEE Trans. Radiat. Plasma Med. Sci.

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“…ML can be classified into three groups based on the way the predictive model learns and accumulates data [20][21][22][23][24].…”

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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“…rather it changes our previous beliefs regarding the inability to integrate high-dimensional data and identify unique properties which may otherwise affect our perception of parameters. 4 Nonetheless, the study was retrospective and did not occur in real time. 4…”

Section: Machine Learning In Nuclear Cardiologymentioning

confidence: 99%

“…32 Used with permission from Elsevier.Artificial Intelligence in Cardiac Imaging are discrepancies between each institution. Each center has their own classification, protocol, and different acquisition protocols 4. If ML algorithms are universally accepted, there are subsequent difficulties in clinical implementation, and maintenance of data quality.…”

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confidence: 99%

Artificial Intelligence in Cardiac Imaging

Seetharam

Shrestha

Sengupta

2020

US Cardiology Review

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Machine learning (ML), a subset of artificial intelligence, is showing promising results in cardiology, especially in cardiac imaging. ML algorithms are allowing cardiologists to explore new opportunities and make discoveries not seen with conventional approaches. This offers new opportunities to enhance patient care and open new gateways in medical decision-making. This review highlights the role of ML in cardiac imaging for precision phenotyping and prognostication of cardiac disorders.

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Machine learning for nuclear cardiology: The way forward

Cited by 35 publications

References 21 publications

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

A Review of Deep-Learning-Based Approaches for Attenuation Correction in Positron Emission Tomography

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

Artificial Intelligence in Cardiac Imaging

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