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Deep Learning and Analysis of Cardiovascular Imaging
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Author(s): Salvador Castillo-Rivera (Universidad Carlos III de Madrid, Spain) and Ana María Castillo-Rivera (University of Jaén, Spain)
Copyright: 2023
Pages: 15
Source title:
Design and Control Advances in Robotics
Source Author(s)/Editor(s): Mohamed Arezk Mellal (M'Hamed Bougara University, Algeria)
DOI: 10.4018/978-1-6684-5381-0.ch013
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Abstract
Artificial intelligence in the healthcare field has gotten the attention of professionals and researchers because the automated analysis of medical images can enhance diagnosis and therapy considerably. Deep learning (DL) puts forwards tools to deal with this kind of problem. Data science is presumable to address significant changes in the study of cardiovascular imaging allowing change appreciably in the next years encouraged by this learning. For medical professionals, it is fundamental to keep track of the development to make sure that it can have a significant impact on clinical practice. DL in medical image analysis is an interdisciplinary area that requires the support of medical experience and computer techniques. Nevertheless, the optimal clinical application of DL needs tricky formulation of problems, selection of the most proper algorithms and data, and balanced interpretation of outcomes. A current challenge comes from echocardiography. The need for human interpretation has restricted echocardiography's capability for precision medicine.
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