Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices.
1. Chapter 1 : Introduction to biomedical signals and biomedical imaging - p. Page xii 2. Chapter 2 : Medical applications of artificial intelligence - p. Page 51 3. Chapter 3 : Decipher the mask-induced cardiac changes in the ECG signals using DWT and machine learning classifiers - p. Page 74 4. Chapter 4 : A robust to noise classification method for the heart sound signals using deep learning technique - p. Page 97 5. Chapter 5 : A hybrid ResNet-18-UNet model for MRI brain tumor segmentation - p. Page 119 6. Chapter 6 : Artificial intelligence (AI) in medical robotics - p. Page 137 7. Chapter 7 : Artificial intelligence in medical education - p. Page 164 8. Chapter 8 : Artificial intelligence-based obstructive sleep apnea detection using ECG signals - p. Page 178 9. Chapter 9 : Artificial intelligence techniques for diagnosis of atrial fibrillation - p. Page 198 10. Chapter 10 : Artificial intelligence in monitoring and correction of functional state based on electrocardiosignal - p. Page 226 11. Chapter 11 : Deep learning methods for drug repurposing through heterogeneous data - p. Page 289 12. Chapter 12 : Explainable AI for medical applications - p. Page 311 13. Chapter 13 : Artificial intelligence-based smart devices for biomedical applications - p. Page 335 14. Chapter 14 : Automation in orthodontics and orthopedics using artificial intelligence - p. Page 354 15. Chapter 15 : Data-driven model for healthcare diagnosis - p. Page 378 16. Chapter 16 : Artificial intelligence in diabetes management - p. Page 392 17. Chapter 17 : 2D and 3D segmentation of organs using artificial intelligence - p. Page 428 18. Chapter 18 : Early-stage identification of autism in children using gesture monitoring based on artificial intelligence - p. Page 483 19. Chapter 19 : Artificial intelligence in diagnosis of neural disorders using biosignals and imaging - p. Page 518 20. Chapter 20 : Biomedical image security - p. Page 554 21. Index - p. Page 579
Artificial Intelligence in health care has become one of the best assisting techniques for clinicians in proper diagnosis and surgery. In biomedical applications, artificial intelligence algorithms are explored for bio-signals such as electrocardiogram (ECG/ EKG), electrooculogram (EOG), electromyogram (EMG), electroencephalogram (EEG), blood pressure, heart rate, nerve conduction, etc., and for bio-imaging modalities, such as Computed Tomography (CT), Cone-Beam Computed Tomography (CBCT), MRI (Magnetic Resonance Imaging), etc. Advancements in Artificial intelligence and big data has increased the development of innovative medical devices in health care applications. Recent Advances in Artificial Intelligence: Medical Applications provides an overview of artificial intelligence in biomedical applications including both bio-signals and bio-imaging modalities. The chapters contain a mathematical formulation of algorithms and their applications in biomedical field including case studies. Biomedical engineers, advanced students, and researchers can use this book to apply their knowledge in artificial intelligence-based processes to biological signals, implement mathematical models and advanced algorithms, as well as develop AI-based medical devices.
1. Chapter 1 : Introduction to biomedical signals and biomedical imaging - p. Page xii 2. Chapter 2 : Medical applications of artificial intelligence - p. Page 51 3. Chapter 3 : Decipher the mask-induced cardiac changes in the ECG signals using DWT and machine learning classifiers - p. Page 74 4. Chapter 4 : A robust to noise classification method for the heart sound signals using deep learning technique - p. Page 97 5. Chapter 5 : A hybrid ResNet-18-UNet model for MRI brain tumor segmentation - p. Page 119 6. Chapter 6 : Artificial intelligence (AI) in medical robotics - p. Page 137 7. Chapter 7 : Artificial intelligence in medical education - p. Page 164 8. Chapter 8 : Artificial intelligence-based obstructive sleep apnea detection using ECG signals - p. Page 178 9. Chapter 9 : Artificial intelligence techniques for diagnosis of atrial fibrillation - p. Page 198 10. Chapter 10 : Artificial intelligence in monitoring and correction of functional state based on electrocardiosignal - p. Page 226 11. Chapter 11 : Deep learning methods for drug repurposing through heterogeneous data - p. Page 289 12. Chapter 12 : Explainable AI for medical applications - p. Page 311 13. Chapter 13 : Artificial intelligence-based smart devices for biomedical applications - p. Page 335 14. Chapter 14 : Automation in orthodontics and orthopedics using artificial intelligence - p. Page 354 15. Chapter 15 : Data-driven model for healthcare diagnosis - p. Page 378 16. Chapter 16 : Artificial intelligence in diabetes management - p. Page 392 17. Chapter 17 : 2D and 3D segmentation of organs using artificial intelligence - p. Page 428 18. Chapter 18 : Early-stage identification of autism in children using gesture monitoring based on artificial intelligence - p. Page 483 19. Chapter 19 : Artificial intelligence in diagnosis of neural disorders using biosignals and imaging - p. Page 518 20. Chapter 20 : Biomedical image security - p. Page 554 21. Index - p. Page 579
