Artificial Intelligence in Precision Cardiovascular Medicine: Pubrica.com

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

2. Today'sDiscussion Outline ofTopics Inbrief Introduction Machine Learning Deep Learning CognitiveComputing Conclusion FutureScopes

3. InBrief Artificial intelligence is the new found solution for all societal and technological problems. AI has the capacity to enhance the statistics and provide with data on the hidden information. The significance of AI in cardiovascular diseases are still in the early stages. AI appears to be promising in Cardiovascular disease screening, diagnosing and prediction of higherrisks.

4. Introduction The 21st century is the century of inventions and artificial intelligence is the most important one among themall. The use of artificial intelligence in various fields like science and technology, education ,engineering and manyothers. Cardiovascular diseases are a complex phenomenon and each patient is different , the large available data from each resource is diverse and an analysis of the same is very difficult here is were artificial intelligence would play a vitalrole. In the big data era, AI techniques, such as machine learning, are revolutionizing the way of how the physicians administer clinical treatment and make clinicaldiagnosis.

5. MachineLearning Machine learning has been categorized into 3 major learning types, which are unsupervised; supervised; and reinforcement. Supervised learning, algorithms usually use setsofdata labeled by humans in order to predict the known and thus the desiredoutcome. Unsupervised learning is to genotypes, identify the novel disease mechanisms,or phenotypes from unknown patterns present in thedata.

6. Deep Learning Deep learning is also similar to the operations of the human brain which uses the different layers of the artificial neuronal networks that could however generate automated predictionsfromthe input. Deep learning has proven to be good, even better than the other machine-learning techniques, such as SVM, because deep learning could be using multiple layers as well for the transformations, when compared with theallthe 2 layers used by theSVM.

7. CognitiveComputing Cognitive computingcan involve all the self-learning systems such as machine learning , process of recognizing the various patterns and the natural language processing to mimic the operationof human thoughtprocesses. In the terms of cognitive computing, a device is given training by deep-learning algorithmsor machine-learning. Cognitive computing aims at creating an automated computerized models that cansolve problems without humanassistance. IBM Watson, is a well-known example of cognitive computing, continuously learns from thesets of data and can also predict the outcome using the various and multiplealgorithms.

8. Conclusion The analysis of the big data and the implementation of the deep learning have a great potential for detecting and diagnosing novel phenotypes and genotypes in heterogeneous CVdiseases. Such as familial AF, white-coat hypertension, pulmonary hypertension,HFpEF , Brugada syndrome, HTN, and metabolicsyndrome. Cognitive computers, such as IBM Watson, willnotjust be a standard in health care facilities and assist physicians with their decision making and prediction of patientoutcomes. AI can and will never replace physicians, however it is important that physicians should know to use AI in the right manner and to generate their diagnosis , process big data analytics, and thus personliaze AIapplications.

9. Limitations AI techniques might be utilized to take care of more mind boggling issues than customary insights, however the utilization of these strategies is appropriately increasinglyperplexing. Other than a fundamental comprehension of the procedures, it requires adequate programming abilities to set up a model, and the information to decipher yield from demonstrative instruments to assess model execution and prevent modeloverfitting. A significant evaluation that is discovering its way along the utilization of AI in clinical research issues is the estimation of an issue's multifacetednature. The recognizable proof of datasets and goals that can profit by AI execution is a developing need in current diagnostic workprocesses.

10. FutureScopes In vast areas of interest,AI has a great potential wereitcan be used for the support toolsclinically , AI is already performing on a par orevenmuch better than humanexperts. Implementations in thefieldof imaging and ECG systems could be expected in hospitals very shortly, improving the reproducibility and the overall accuracy ofmeasurements. ML modelsthatuse imaging or ECG data inorder to predict coronary heart disease could help prevention of the unnecessary cardiaccatheterisations. Implementation oftheAI toolsto detect arrhythmias in a better manner and more subtle ECG abnormalities could facilitate the better riskstratification.

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