Copyright © 2020 pubrica. All rights reserved 1
Artificial Intelligence in Cardiovascular Imaging
Dr. Nancy Agens, Head,
Technical Operations, Pubrica
In brief
Cardiovascular disease continues to be the
world's most common cause of morbidity
and mortality. The importance of artificial
intelligence methods including cognitive
computing, deep learning, and machine
learning, show their uses in cardiology.
The incorporation into daily decision-
making of artificial intelligence tools in the
field of cardiology will improve care.
Artificial intelligence is showing great
promise in the field of cardiovascular
imaging.
I. INTRODUCTION
Cardiovascular disease continues to
be the world's most common cause of
morbidity and mortality, and is therefore a
major focus for medical imaging and
medical research. Notwithstanding
continuous advances in modalities of cardiac
imaging, including cardiac computed
tomography, cardiovascular magnetic
resonance and echocardiography, the heart
remains a challenging organ to photograph,
particularly because of its perpetual
motion(de Marvao et al., 2020). Artificial
intelligence (AI) defines a computational
program capable of performing tasks which
are typical human intelligence
characteristics including problem solving,
sound, recognizing objects, understanding
language, planning and pattern identification
and recognition. Practically AI described as
a device or machine ability to make
decisions autonomously based on it received
data. There are four main areas of IA
implementation including 1. Computer-
aided detection 2. Quantitative analysis tools
3. Clinical decision support and 4.
Computer-aided diagnosis.In medical field,
the AI used for choose the best treatment
option, new disease identification, being
used to forecast a possible diagnosis, etc.
(Darcy et al., 2016) and (Dey et al., 2019).
In the management of cardiovascular
disease, the cardiovascular imaging is
paying an important role. When new
imaging techniques are being continually
implemented this has only been reinforced.
More and more studies in cardiac
imaging are being carried out each year.
This is informed by various factors such as
increased imaging recognition, which has
played an incremental role in the
monitoring, management and diagnosis of
patient outcomes over the years.
Furthermore, imaging was broader
accessible, and not only has the imaging
equipment become more accurate, but also
cheaper and faster. The improved
interpretability and quality of imaging
studies not only resulted in increased patient
satisfaction, but could also lead to enhanced
legal and clinical reassurance for the doctor.
From an economic point of view, the global
rise in costs of healthcare is partly related to
the imaging units enhancing number in the
hospital and hence the enhanced number of
imaging studies carried out (Papanicolas et
al., 2018). The extension of imaging
techniques and subsequent analyzes
nevertheless exceeds the average imaging
specialist's efficiency limits. The solution
for the systematic assessment of the rising
number of medical images is medical
artificial intelligence (AI). The smart
computers using AI can provide assistance
Copyright © 2020 pubrica. All rights reserved 2
and guidance during image evaluation and
acquisition has begun to show scientific
literatures. This may have influences
significantly on the workload of the
physician (Siegersma et al., 2019).
The incorporation of various AI
technologies will support cardiac imaging.
Physicians will then be able to concentrate
on activities best accomplished by human
intelligence without being disturbed by tasks
that can be automated. This can lead to
improved health equality, improved
healthcare delivery quality, improved
patient-physician relationships, higher job
satisfaction for physicians and access to
healthcare while limiting the unsustainable
increase in healthcare expenditure in an
aging population.
II. ARTIFICIAL INTELLIGENCE (AI)
USE OF CARDIOVASCULAR IMAGING
ECHOCARDIOGRAPHY
The most commonly used modality
of imaging in cardiology is
echocardiography. AI will help to reduce
user reliance in a more systematic study of
echocardiographic images. The ability to
help analysis echo images has already been
demonstrated, enables the generation of
important cardiac variables on - the-fly with
automated echocardiographic view
classification (Siegersma et al., 2019).
III. COMPUTED TOMOGRAPHY
In the last decade, Cardiac CT has
made a leap forward with an emphasis on
the visualization of stenosis in the coronary
tree, coronary calcification, plaque
characteristics and scoring and, more
recently, flow modeling. Automated noise
reduction is exciting prospects for AI in CT;
while preserving optimum quality of image
and minimizing invasive coronary
angiography (ICA) for severe stenosis
determination (Wolterink et al., 2017).
IV. MAGNETIC RESONANCE IMAGING
Cardiac MRI is an area in which
many parts of the heart are imaged including
myocardial characterization, perfusion
imaging, flow imaging, contractile function
and anatomical imaging. Nevertheless,
considering the many possibilities provided
by cardiac MRI for AI applications and
technology methods employed in MRI,
radiographers with expertise and physics
knowledge and cardiac anatomy is central to
the processing and study of images. The
accuracy of cardiac MR images is therefore
not only dependent on the customer but also
dependent on the scanner, patient and
vendor(Ferreira et al., 2014).
V. KEY FEATURES
Missed diagnoses, efficiency problems
and timing problems found at all
imaging chain stages. AI technology will
maximize performance and decrease
costs at all levels of decision-making,
interpretation and image acquisition.
“Big data” from imaging will interface
with high data volumes from the
pathology and electronic health record to
provide new opportunities and insights
to personalizing treatment.
Image interpretation, diagnostic support
and disease phenotyping is the main
areas of AI for imaging. Cluster review
of applicable imaging and clinical
knowledge can provide opportunities to
better identify disease. Automatic
measurements and automatic image
segmentation will provide the diagnostic
support. Initial steps toward automatic
image analysis and acquisition are being
taken.
Copyright © 2020 pubrica. All rights reserved 3
VI. CONCLUSION
AI technologies like cognitive
computing, deep learning and machine
learning are exciting and indeed they can
change the way cardiology is practiced,
particularly in the field of cardiac imaging.
Physicians need to be prepared for the
coming AI era, however, and clear tests of
AI's effectiveness within daily practice are
important. The opportunity is exciting in
terms of cost-effectiveness, equality and
quality to make our healthcare systems
stronger.
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