Meta-analysis in the field of cardiovascular imaging using artificial intelligence - Pubrica

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.

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.

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

• Ultrasounds are portable
• More standardized analysis
• The precise interpretation of data
• Speed
• Can be easily affordable

Computer Tomography:

Computer tomography in cardiovascular imaging has shown growth over the past 10 years. Some advantages of cardiac CT are

? 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.

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.

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

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

Conclusion

The cardiovascular imaging has shown remarkable growth over the past few years. It not only gives structural data but also physiological and molecular features of the heart.

Full Information: https://bit.ly/2FvQ68c

Reference: https://pubrica.com/services/research-services/meta-analysis/

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