Cardiovascular disease (CVD) is responsible for a third of all deaths in women and more than a half in men. Despite continuous improvements in treatment devices and imaging, there is still a rise in the morbidity rate from CVDs each year. Compiled by experts in the field, a thorough investigation is given to current topics and problems relating to CVD, which will enable the scientific and medical communities to search for the most effective strategies for dealing with these diseases. As one of the most prominent diseases in our society, CVD requires dedicated analysis and investigation in order to reduce the mortality rate worldwide. Scholars, biomedical engineers and medical practitioners will greatly benefit from the detailed information in this book as it will give a better understanding of the causes, diagnosis and treatment of CVD.
Inhaltsverzeichnis
Volume 2
1 – Coronary and carotid artery calcium detection, its quantification, and grayscale morphology-based risk stratification in multimodality big data: a review
2 – A cloud-based smart LD measurement tool for stroke risk assessment during multicenter clinical trials
3 – Multiresolution-based Coronary Calcium Volume Measurement Techniques from Intravascular Ultrasound Videos
4 – Deep Learning Fully Convolution Network for Lumen Characterization in Diabetic Patients using Carotid Ultrasound: A tool for Stroke Risk
5 – Deep Learning Strategy for Accurate Carotid Intima-Media Thickness measurement: an Ultrasound Study on Japanese Diabetic Cohort
6 – MEMS based manufacturing technique of vascular bed
7 – Risk of Coronary Artery Disease: Genetics and External Factors
8 – Wall Quantification and Tissue Characterization of Coronary Artery
9 – Rheumatoid Arthritis: Its link to Atherosclerosis Imaging and Cardiovascular Risk Assessment using Machine Learning-based Tissue Characterization
10 – Echolucency-based Phenotype in Carotid Atherosclerosis Disease for Risk Stratification of Diabetes Patients
11 – Plaque Tissue Morphology-based Stroke Risk Stratification using Carotid Ultrasound: A Polling-based PCA Learning Paradigm
12 – Morphologic TPA (m TPA) and Composite Risk Score for Moderate Carotid Atherosclerotic Plaque is strongly associated with Hb A1c in Diabetes Cohort imminently
Über den Autor
Petia Radeva is a senior researcher and full professor at the University of Barcelona, where she is also the head of the Computer Vision and Machine Learning Consolidated Research Group (CVUB), as well as the head of Medical Imaging Laboratory (Mi Lab) of Computer Vision Centre, Spain. Her research interests include the development of deep learning, computer vision and lifelogging, and their applications to healthcare. Radeva is an IAPR Fellow, and she has received Icrea Academia and the CIARP Aurora Pons Porrata awards.Jasjit S Suri is an innovator, scientist, industrialist and an internationally known world leader in biomedical engineering, sciences and its management. He has written numerous publications and is currently the chairman of Athero Point, USA, dedicated in stroke and cardiovascular imaging. He is a recipient of Life Time Achievement Award by Marquis (2018) and a Fellow of the American Institute of Medical and Biological Engineering (2004).
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