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https://www.selleckchem.com/products/pimicotinib.html The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging.PURPOSE Coronary computed tomography angiography (CCTA) has its limitations in evaluating arteries with stents or heavy calcification. This study compares the diagnostic performance of subtracted coronary computed tomography angiography (SCCTA) and nonsubtracted coronary computed tomography angiography (NSCCTA) in evaluating coronary artery disease (CAD) and in-stent restenosis (ISR). MATERIALS AND METHODS Twelve patients with stents and 20 patients with heavy coronary calcifications (total Agatston's score >400) underwent both SC

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