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An Overview of Meta-Analysis Inthe Field of Cardiovascular Imaging

using Artificial Intelligence

Dr. Nancy Agens, Head,

Technical Operations, Pubrica

sales@pubrica.com

In Brief

The most plausible human endeavour

happens in the healthcare sector, has the

greatest impact on artificial intelligence.

Artificial intelligence possesses

superhuman performance in diagnosis,

treatments, clinical testings, etc. AI will

completely change the era of medicine by

doctors, mainly in cardiology and

radiology. Pubrica is conducting a meta-

analysis in quantitative research about

cardiovascular imaging to help future

medical researchers and doctors.

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I. INTRODUCTION

As years passing with growing technology,

cardiac diagnostics have potential growth

simultaneously. A huge population starts

accepting imaging techniques for

diagnosis and monitoring treatment in

healthcare sectors that are faster and can

be easily affordable. The interpretation of

imaging is more accurate for satisfying

patients. Writing a meta-analysis about

cardiovascular imaging will be useful for

future studies. Though cardiology has

implanted many numbers of cases using

AI, it is growing recently in the field of

medicine. This blog brings out the

diagnostic tools of cardiology using

artificial intelligence.

II. A META-ANALYSIS OF

CARDIOVASCULAR IMAGING

Echocardiography

Computed tomography

Cardiac MRI

Nuclear imaging

Future aspects

Echocardiography

Echocardiography, as the name, suggests it

will diagnose by ultrasounds. The main

uses of echocardiography are

o Ultrasounds are portable

o More standardized analysis

o The precise interpretation of

data

o Speed

o Can be easily affordable

However, it is a user-dependent tool. AI

has stepped to different echo cardiographic

imaging chain. It has automated

identification of left ventricles by having

algorithms for congenital disorders and

diseases. Some of the other important

diagnosis is phenotypic heart failure and

hypertrophic cardiomyopathy. In general,

it will lead to new hypotheses and perform

a better diagnosis and prognosis in

different cardiac diseases.

Computer Tomography

Computer tomography in cardiovascular

imaging has shown growth over the past

10 years. Some advantages of cardiac CT

are

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Reduces noise

Better image quality

No need for invasive coronary

angiography for diagnosing

stenosis

The meta-analysis experts say that the

cardiac CT worked by using an artificial

neural network model which determine the

level of calcium from coronary CT

angiography. Another application of

Cardiac CT is to process images. The

visualization of images can be achieved by

the machine learning process. Unlike

echocardiography, Cardiac CT is user-

independent and fast. The major

significance is to reduce radiation

exposure to the patients and helps to create

personalized medicine.

Cardiac MRI

Imaging the heart from various parameters

is done by cardiac Magnetic resonance

imaging.

Functions

Flow imaging

Perfusion imaging

Anatomical imaging

Myocardial characterizations

Contractions

The AI significance can be performed only

by radiographers that have experience in

physics and cardiac anatomy as they are an

integral part of image analysis. However,

the quality of the image is both user and

vendor dependent.

The main objectives of cardiac MRI

Automated segmentation of heart

structure

Infarct tissue analysis.

The studies carried out by Cardiac MRI

are

Component analysis in pulmonary

hypertension for patients

Worsening of left ventricular

function for patients with

a repaired tetralogy of Fallot

Due to these major disadvantages, MRI

has become more challenging in imaging

than others. Researchers are performing

with various ideas to overcome those

challenges.

Nuclear imaging

Nuclear imaging in cardiology is used to

determine the faults in the myocardium

wall.

Methods

Myocardial perfusion single-

photon emission computed

tomography (spect)

Positron emission tomography(pet)

1. SPECT

SPECT detects the gamma rays emitted by

the radioactive tracer to reconstruct the

tissue. SPECT is used to diagnose the

abnormal myocardium and it is interpreted

using Artificial neural network models.

The accuracy of data was boosted by

machine learning.

It also detects

Stress

Stress-induced ischaemia

Rest defects

2. PET

PET detects the two concurrent opposite

annihilation photos. Both spect and PET

are similar to CT and MRI.

Disadvantages

It leads to radiation exposure in humans

III. FUTURE ASPECTS

There will be a huge opportunity for AI

implementation in future research from

machine learning sources.

Biomarkers

Genomics

Proteomics

Metabolomics

This can improve the healthcare standard

and quality in the treatment of patients.

The future researchers can work on the

challenges of the imaging techniques using

meta-analysis writing services

VI. CONCLUSION

The cardiovascular imaging has shown

remarkable growth over the past few years.

It not only gives structural data but also

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physiological and molecular features of the

heart. AI set up a huge platform to

healthcare from past to present and even in

future. Pubrica established a meta analysis

of artificial intelligence in cardiovascular

imaging.

REFERENCES

1. Siegersma, K. R., Leiner, T., Chew, D. P.,

Appelman, Y., Hofstra, L., &Verjans, J. W. (2019).

Artificial intelligence in cardiovascular imaging:

state of the art and implications for the imaging

cardiologist. Netherlands Heart Journal, 1-11.

2. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M.,

&Kitai, T. (2017). Artificial intelligence in

precision cardiovascular medicine. Journal of the

American College of Cardiology, 69(21), 2657-

2664.

3. Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D.,

Shrestha, S., Sengupta, P. P., & Marwick, T. H.

(2019). Artificial intelligence in cardiovascular

imaging: JACC state-of-the-art review. Journal of

the American College of Cardiology, 73(11), 1317-

1335.

4. Johnson, K. W., Soto, J. T., Glicksberg, B. S.,

Shameer, K., Miotto, R., Ali, M., ...& Dudley, J. T.

(2018). Artificial intelligence in cardiology. Journal

of the American College of Cardiology, 71(23),

2668-2679.