Alireza Khodadadiyan, Hamed Bazrafshan Drissi, Mina Mashayek, Helia Bazroodi and Mehdi Shaban
Shiraz University of Medical Sciences, Iran
Scientific Tracks Abstracts: Gen Med (Los Angeles)
Acute Coronary Syndrome (ACS) is the mainspring of fatal complications and mortality. The fundamental process related to ACS is the expansion of clots overlaying a cracked or damaged plaque, contributing to obstruction of vessels severely and consequently, myocardial ischemia. The ruptured plaque consists of a sizable number of platelets originates from a thrombus; moreover, platelet accumulation and plaque rupture can trigger coagulation pathways. Artificial intelligence (AI) is characterized as a series of algorithms leading to put effort into mimicking human intelligence. Machine-learning underlying artificial intelligence has one of the most practical techniques, defined as deep learning. In the cardiovascular field of medicine, applications of AI are machine learning approaches for diagnostic procedures and can be utilized for various purposes, including forecasting results after revascularization procedures, prediction of risk for cardiovascular diseases, novel drug objectives and cardiovascular imaging. AI has presented significant potential advantages in patients with ACS via machine learning. From diagnosis to treatment impacts to foreseeing adverse events, subsequently augmented bleeding risk and mortality rate in patients with ACS, since ACS cause endothelial dysfunction and vascular inflammation, machine learning ought to discover a fundamental spot in clinical medication and in interventional cardiology for the treatment and anticoagulation management in patients with ACS.
Alireza Khodadadiyan doing my undergraduate at Shiraz University of Medical Sciences. I have three published books indexed in Amazon and Open Aire and three congressional papers so far. I have been a senior cardiovascular mentor for two years as I am really interested in this field of medicine.
