Full Description
Artificial intelligence in healthcare 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, and nerve conduction, and for bio-imaging modalities, such as computed tomography (CT), cone-beam computed tomography (CBCT), and MRI (magnetic resonance imaging). Advancements in artificial intelligence and big data have increased the development of innovative medical devices in healthcare applications. Advances in Artificial Intelligence: Biomedical Engineering Applications in Signals and Imaging 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 the biomedical eld, 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, and develop AI-based medical devices.
Contents
1. Introduction to Biomedical signals and Biomedical Imaging
2. Medical Applications of Artificial Intelligence
3. Decipher the Mask-Induced Cardiac Changes in the ECG Signals using DWT and Machine Learning Classifiers
4. A robust to noise classification method for the heart sound signals using deep learning technique
5. Brain tumour segmentation using Advanced deep learning Techniques
6. AI in medical robotics
7. AI in medical education
8. Machine Learning and Deep Learning Techniques for Smart Diagnosis of Obstructive Sleep Apnea using Single lead ECG
9. Artificial Intelligence techniques for Diagnosis of Atrial Fibrillation
10. Artificial intelligence in monitoring and correction of functional state based on electro-cardio signal
11. Deep Learning for drug repurposing.
12. Explainable AI for medical applications
13. Artificial intelligence-based smart devices for biomedical applications
14. Automation in the field of orthodontics and orthopaedics based on artificial Intelligence
15. Data driven models in health care diagnosis
16. Artificial Intelligence for diabetes management
17. 2D and 3D segmentation of organs using artificial intelligence
18. Early-stage identification of Autism in children using gesture monitoring based on Artificial Intelligence.
19. Artificial Intelligence in diagnosis of Neural disorders using bio signals and Imaging
20. Biomedical Image Security
21. AI for Cancer Diagnosis
