Advances in Artificial Intelligence

Biomedical Engineering Applications in Signals and Imaging

Kunal Pal,Bala Chakravarthy Neelapu,J. Sivaraman

• Verlag: Academic Press
• Jahr: 2024
• ISBN:
  9780443153921
• Thema: Ciencias biológicas
• Thema: Biotecnología

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.

• Covers the recent advancements of artificial intelligence in healthcare, including case studies on how this technology can be used
• Provides an understanding of the design of experiments to validate the developed algorithms
• Presents an understanding of the versatile application of artificial intelligence in bio-signal and bio-image processing techniques

Inhaltsverzeichnis

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Preface
• Chapter 1 Introduction to biomedical signals and biomedical imaging
• Chapter 2 Medical applications of artificial intelligence
• Chapter 3 Decipher the mask-induced cardiac changes in the ECG signals using DWT and machine learning classifiers
• Chapter 4 A robust to noise classification method for the heart sound signals using deep learning technique
• Chapter 5 A hybrid ResNet-18-UNet model for MRI brain tumor segmentation
• Chapter 6 Artificial intelligence (AI) in medical robotics
• Chapter 7 Artificial intelligence in medical education
• Chapter 8 Artificial intelligence-based obstructive sleep apnea detection using ECG signals
• Chapter 9 Artificial intelligence techniques for diagnosis of atrial fibrillation
• Chapter 10 Artificial intelligence in monitoring and correction of functional state based on electrocardiosignal
• Chapter 11 Deep learning methods for drug repurposing through heterogeneous data
• Chapter 12 Explainable AI for medical applications
• Chapter 13 Artificial intelligence-based smart devices for biomedical applications
• Chapter 14 Automation in orthodontics and orthopedics using artificial intelligence
• Chapter 15 Data-driven model for healthcare diagnosis
• Chapter 16 Artificial intelligence in diabetes management
• Chapter 17 2D and 3D segmentation of organs using artificial intelligence
• Chapter 18 Early-stage identification of autism in children using gesture monitoring based on artificial intelligence
• Chapter 19 Artificial intelligence in diagnosis of neural disorders using biosignals and imaging
• Chapter 20 Biomedical image security
• Index