The Role of Artificial Intelligence In Cardiovascular Imaging: Pubrica.com

1. ARTIFICIAL INTELLIGENCE IN CARDIOVASCULAR IMAGING An Academic presentationby Dr. Nancy Agens, Head, Technical Operations, Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion OUTLINE OFTOPICS In brief Introduction Artificial Intelligence (Ai) Use Of Cardiovascular ImagingEchocardiography ComputedTomography Magnetic ResonanceImaging KeyFeatures Conclusion

3. Inbrief Cardiovascular disease continues to be the world's most common cause of morbidity and mortality. The importance of artificial intelligencemethods including cognitive computing, deep learning, and machine learning, show their usesincardiology. 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 cardiovascularimaging.

4. 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 medicalresearch. 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 perpetualmotion. 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 andrecognition. Contd..

5. There are four main areas of AIimplementationincluding Computer-aided detection, Quantitative analysis tools, Clinical decision support and Computer- aideddiagnosis. In medical field, the AI used for choose the best treatment option, new disease identification, being used to forecast a possiblediagnosis. In the management of cardiovascular disease, the cardiovascular imaging is paying an importantrole. When new imaging techniques are being continually implemented this has only beenreinforced.

6. Artificial Intelligence (Ai) Use Of Cardiovascular Imaging Echocardiography The most commonly used modality of imaging in cardiology isechocardiography. AI will help to reduce user reliance in a more systematic studyof echocardiographicimages. The ability to help analysis echo images has already been demonstrated, enables the generation of important cardiac variables on - the-fly with automated echocardiographic viewclassification.

7. 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, flowmodeling. 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.

8. Magnetic ResonanceImaging Cardiac MRI is an area in which many parts of the heart are imaged including myocardial characterization, perfusion imaging, flow imaging, contractile function and anatomicalimaging. Nevertheless, considering the many possibilities provided by cardiac MRIfor AI applications and technology methods employed in MRI, radiographers with expertise and physics knowledge and cardiac anatomy is central to the processing and study ofimages. The accuracy of cardiac MR images is therefore not only dependent on the customer but also dependent on the scanner, patient andvendor.

9. KeyFeatures 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 identifydisease.

10. 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 cardiacimaging. Physicians need to be prepared for the coming AI era, however, and clear tests of AI's effectiveness within daily practice areimportant. The opportunity is exciting in terms of cost-effectiveness, equality and quality to make our healthcare systemsstronger.

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