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

1. 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 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. Keywords: Meta-analysis Services, meta-analysis paper writing, writing a meta-analysis, how to write a meta-analysis, write a meta-analysis paper, meta-analysis experts, writing a meta-analysis paper, conducting a meta- analysis, meta-analysis research, meta- analysis in quantitative research, meta- analysis research help, how to write meta-analysis, Meta-analysis Services, I. INTRODUCTION As years passing with growing technology, cardiac diagnostics have potential growth simultaneously. A huge population starts accepting imaging 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 oUltrasounds are portable oMore standardized analysis oThe precise interpretation of data oSpeed oCan be easily affordable However, it is a user-dependent tool. AI has stepped to different echo cardiographic imaging chain. It 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 possesses Writing Writing has automated techniques for Copyright © 2020 pubrica. All rights reserved 1

2. Reduces noise Better image quality No need for invasive coronary angiography 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 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 significance is to 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 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 photon emission 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 for diagnosing perfusion single- computed application of fast. reduce The major radiation patients with Copyright © 2020 pubrica. All rights reserved 2

3. 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. Copyright © 2020 pubrica. All rights reserved 2