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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