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This comprehensive reference work details the latest research and developments in the utilization of AI strategies in the diagnosis and treatment of COVID-19 patients. It explores aspects of the early assessment of lung functions in coronavirus patients, as well as the incorporation of AI and Machine Learning paradigms. The text also reflects on the clinical implications of AI in COVID-19 and addresses miscellaneous topics related to the complexities and challenges of COVID-19 therapeutics. This is an essential text for academics, clinicians, and scientists working in the domain of lung cancer, data-mining, machine learning, and deep learning within the COVID-19 environment.
Key Features:
- Comprehensive overview of the implementation of artificial intelligence (AI) and machine learning (ML) strategies to the diagnosis, follow on/follow-up, and treatments of COVID-19 patients.
- A number of authors and contributors are front-line researchers and clinicians in COVID-19 patient affairs from around the world.
- Provides unique coverage of AI and Machine Learning in the prediction of bloodclotting in COVID-19 patients.
- Offers specific examples of diagnosing COVID-19 with AI utilization within CT.
- Presents extensive references at the end of each chapter to enhance further study.
Ayman El-Baz is a Distinguished Professor at University of Louisville, Kentucky, United States and University of Louisville at Alamein International University (UofL-AIU), New Alamein City, Egypt. He earned his Ph.D. in electrical engineering from the University of Louisville in 2006. Dr. El-Baz was named as a Fellow for IEEE, Coulter, AIMBE and NAI for his contributions to the field of biomedical translational research. Dr. El-Baz has almost two decades of hands-on experience in the fields of bio-imaging modeling and non-invasive computer-assisted diagnosis systems. He has authored or coauthored more than 700 technical articles.
Jasjit S. Suri is an innovator, scientist, visionary, and internationally recognized leader in biomedical engineering. With over 25 years in biomedical devices and management, he earned his Ph.D. from the University of Washington and a Business Management degree from Weatherhead, Case Western Reserve University. Dr. Suri received the President’s Gold Medal in 1980, became a Fellow of the American Institute of Medical and Biological Engineering, and earned the Marquis Lifetime Achievement Award in 2018.
Preface
Acknowledgments
Editor biographies
List of contributors
1 Role of deep learning strategies in detecting COVID-19 pneumonia
2 Radiography against CT images for detection and analysis of COVID-19 in deep learning techniques
3 3D computed tomography-based framework for automated grading of COVID-19 infections
4 Artificial intelligence assisted mechanical ventilation and respiratory failure treatment in COVID-19 patients
5 Machine learning and Internet of Things in early detection of COVID-19 pneumonia
6 Transfer learning, image fusion, and nutritional AI models for COVID-19 analysis
7 Prediction of respiratory support levels in COVID-19 patients using a CT-based system
8 Comprehensive insights into COVID-19: advanced technologies for multi-dimensional understanding and combating the pandemic
9 COVID-19 detection from chest CT: a survey of machine learning and deep learning approaches
10 AI-driven segmentation techniques for COVID-19 pneumonia in medical imaging: a short review and future directions
11 A survey of artificial intelligence techniques for prognosis and severity prediction in COVID-19 infections
12 Challenges, opportunities, and future directions in AI for COVID-19 response