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Artificial Intelligence Techniques for Cardiovascular Disease
Risk Assessment and Prevention
- 1st Edition - December 1, 2099
- Authors: Ankush D Jamthikar, Jasjit Suri
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 3 5 7 - 7
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 3 5 8 - 4
Artificial Intelligence Techniques for Cardiovascular Disease gives detailed insights into CVD risk assessment using non-invasive carotid imaging under the paradigm of artifi… Read more
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Request a sales quoteArtificial Intelligence Techniques for Cardiovascular Disease gives detailed insights into CVD risk assessment using non-invasive carotid imaging under the paradigm of artificial intelligence. The book initially presents global statistics of CVD along with the current standard-of-care screening tools. It provides a deep understanding of carotid atherosclerosis, the anatomy of the carotid artery, how carotid imaging can capture several atherosclerotic plaque characteristics automatically, and the link between these automated carotid artery plaque characteristics with the coronary artery disease. This book further explains the use of carotid atherosclerotic plaque characteristics for short-term and long-term CVD risk assessment using machine learning and deep learning algorithms. A novel approach to CVD risk prediction by combining conventional cardiovascular risk predictors with carotid ultrasound image-based phenotypes is also discussed and compared against the conventional CVD risk prediction algorithms. Lastly, this book provides applications of CVD risk assessment in several comorbidities such as rheumatoid arthritis, chronic kidney disease, diabetes, coronary artery disease, HIV, and COVID-19. Artificial Intelligence Techniques for Cardiovascular Disease is for those interested in using artificial intelligence techniques for CVD and stroke risk assessment.
- Integrates conventional risk factors, blood biomarkers, and image phenotypes to predict coronary artery disease on asymptomatic patients
- Predicts the 10-year cardiovascular risk and estimates the contribution of each biomarker responsible for coronary artery disease
- Features advance machine learning techniques such as ensemble and multi-label strategies for cardiovascular disease risk prediction
Imaging Scientists, Imaging Engineers, Artificial Intelligence scientists, Machine Learning Designers, Fusion of AI systems, Diagnostic Centers having an ultrasound, MRI, CT, Cardiological Diagnostic Clinics
Section I: Introduction 1. Global Statistics on Cardiovascular Disease 2. Atherosclerosis and Arterial Imaging3. Introduction to current standard-of-care CVD risk screening toolsSection II: Carotid Imaging for CVD Risk Assessment4. Automated Phenotype Measurements for Common Carotid Artery5. Carotid Bulb Imaging and their Measurements6. Intraplaque neovascularization as a tool for CVD risk prediction7. Image Phenotype and its link with Coronary Artery DiseaseSection III: 10-year CVD risk Measurement Methods using Carotid Imaging8. 10-year Image Phenotype Measurements9. Composite Risk Estimation10. 10-year risk for Bulb vs. Common Carotid Artery11. Benchmarking 10-year risk against Conventional CalculatorsSection IV: Machine Learning systems for CVD Risk Prediction12. Machine Learning-based CVD Risk Estimation13. Role of Surrogate Biomarkers for CVD risk prediction14. Grayscale feature extraction in ML framework for CVD risk predictionSection V: Artificial Intelligence systems for Multiclass CVD Risk Prediction15. Machine learning system for Multiclass for coronary artery disease risk prediction16. Bulb Imaging for Multiclass coronary artery disease risk prediction17. ML for CVD risk prediction for follow-up patients in Rheumatology application18. Deep Learning for CVD Risk PredictionSection VI: Artificial Intelligence for Survival Analysis in CVD risk prediction19. Survival Analysis using Statistical Approach20. Survival Analysis using Machine Learning 21. Survival Analysis using Deep Learning22. Comparing the ML-based statistical Survival Analysis for CVD risk predictionSection VII: Miscellaneous Applications for CVD Risk Assessment23. CVD risk prediction in Diabetes application24. CVD risk prediction in Rheumatology application25. CVD risk prediction in Coronary artery disease patients26. CVD risk prediction in Chronic Kidney Disease (CKD) patients27. CVD risk prediction in HIV application28. CVD risk prediction in COVID-19
- No. of pages: 450
- Language: English
- Edition: 1
- Published: December 1, 2099
- Imprint: Elsevier
- Paperback ISBN: 9780323913577
- eBook ISBN: 9780323913584
JS
Jasjit Suri
Dr. Jasjit Suri, PhD, MBA, is an innovator, visionary, scientist, and internationally known world leader. Dr Suri received the Director General’s Gold medal in 1980 and Fellow of (i) American Institute of Medical and Biological Engineering, awarded by the National Academy of Sciences, Washington DC, (ii) Institute of Electrical and Electronics Engineers, (iii) American Institute of Ultrasound in Medicine, (iv) Society of Vascular Medicine, (v) Asia Pacific Vascular Society, and (vi) Asia Association of Artificial Intelligence. Dr. Suri was honored with life time achievement awards by Marcus, NJ, USA and Graphics Era University, Dehradun, India. He has published nearly 300 peer-reviewed Artificial Intelligence articles, nearly 2000 Google Scholar Publications, 100 books, and 100 innovations/trademarks leading to an H-index of nearly 100 with about 43,000 citations. He has held positions as chairman of AtheroPoint, CA, USA, IEEE Denver section, Colorado, USA, and advisory board member to healthcare industries and several universities in the United States of America and abroad.
Affiliations and expertise
Chairman, AtheroPoint LLC, USA