Computational Intelligence in Bioprinting

Name: Computational Intelligence in Bioprinting | Challenges and Future Directions
Brand: Wiley
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Challenges and Future Directions

E. Gangadevi M. Lawanya Shri Rajesh Kumar Dhanaraj Balamurugan Balusamy(Herausgeber*in)

Wiley (Verlag)

1. Auflage

Erschienen am 16. Februar 2024

352 Seiten

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Inhalt

Cover
Title Page
Copyright Page
Contents
Preface
Chapter 1 The Emergence of Bioprinting and Computational Intelligence
1.1 Introduction
1.2 Related Study
1.3 Understanding the Basics of Bioprinting and Computational Intelligence
1.3.1 Bioprinting: The Basics
1.3.2 Computational Intelligence: The Basics
1.3.3 Applications of Bioprinting and Computational Intelligence
1.4 The Role of Computational Intelligence in Bioprinting
1.5 Applications of Bioprinting and Computational Intelligence in Medicine
1.6 Bioprinting and Computational Intelligence in Tissue Engineering and Regenerative Medicine
1.7 Advancements in Bioprinting and Computational Intelligence Technologies
1.8 The Ethical and Regulatory Implications of Bioprinting and Computational Intelligence
1.9 The Future of Bioprinting and Computational Intelligence: Opportunities and Challenges
1.10 Case Studies: Bioprinting and Computational Intelligence in Action
1.10.1 Trends in Computational Intelligence and Bioprinting
1.10.2 Challenges in Computational Intelligence and Bioprinting
1.11 Conclusion
References
Chapter 2 Design, Architecture, Implementation, and Evaluation of Bioprinting Technology for Tissue Engineering
2.1 Introduction
2.2 3D Bioprinting
2.3 Material Characteristics
2.3.1 Printability
2.4 Mechanical Properties
2.5 Biomaterials
2.6 Design, Architecture of 3D Bioprinting
2.6.1 Inkjet Bioprinting
2.6.2 Laser-Assisted Bioprinting (LAB)
2.6.3 Extrusion Bioprinting
2.7 3D Bioprinting Tissue Models
2.8 3D Multimaterial Bioprinting-Development of Complex Architectures
2.9 Implementation and Evaluation
2.10 Bone
2.11 Cartilage
2.12 Soft Tissue Engineering
2.13 Vascular Tissue
2.14 Skin
2.15 Biocompatibility and Control of Degradation and Byproducts
2.16 Conclusion
References
Chapter 3 Design and Development of IoT Devices: Methods, Tools and Technologies
3.1 Introduction to IoT Devices and 3D Bioprinting
3.2 Methodology for Designing IoT Devices for 3D Bioprinting
3.3 Additional Considerations in IoT Device Design for 3D Bioprinting
3.4 Tools for Developing IoT Devices for 3D Bioprinting
3.4.1 Microcontrollers and Development Boards
3.4.2 Sensors and Actuators
3.4.3 Communication Protocols
3.4.4 Software Development Kits
3.4.5 Cloud Platforms
3.4.6 3D Printing Software
3.4.7 CAD Software
3.4.8 Simulation Software
3.4.9 Data Analytics Tools
3.4.10 Cybersecurity Tools
3.5 Techniques for Developing IoT Devices for 3D Bioprinting
3.5.1 Agile Development
3.5.2 Rapid Prototyping
3.5.3 Test-Driven Development
3.5.4 Continuous Integration
3.5.5 Modular Design
3.5.6 Power Optimization
3.5.7 Data Processing Techniques
3.5.8 Quality Assurance
3.5.9 Cybersecurity Techniques
3.5.10 Standardization
3.6 Case Studies of IoT Devices for 3D Bioprinting
3.7 Future Directions in IoT Devices for 3D Bioprinting
3.8 Conclusion
References
Chapter 4 AI-Based AR/VR Models in Biomedical Sustainable Industry 4.0
4.1 Introduction
4.2 Mixed Augmented Reality
4.2.1 SDK in Augmented Reality
4.2.2 Application Scope of Augmented Reality
4.2.2.1 Video Capabilities
4.2.2.2 AR Toolkit Technology
4.2.2.3 Quality of Tracking System
4.3 AR Technology
4.3.1 High Level Augmented Reality
4.3.2 Limitations of Enhanced Image
4.3.3 Limitations of CAD Model
4.3.4 Augmented Reality in Manufacturing Sector
4.4 Requirement of Augmented Reality
4.4.1 Capability of AR
4.4.2 Computational Hardware Capabilities
4.4.3 Symbol-Based Tracking Software
4.5 Conclusions
References
Chapter 5 Computational Intelligence-Based Image Classification for 3D Printing: Issues and Challenges
5.1 Introduction
5.2 Brief Concepts
5.2.1 3D Printing Tools
5.2.2 Artificial Intelligence-Based Digital Marketing
5.2.3 Automated Machine Learning Prediction System
5.3 Role of Artificial Intelligence in Industry 4.0
5.3.1 3D Printing Process
5.3.2 Enhancement in Machine Learning
5.3.3 Genetics-Based Machine Learning
5.3.4 Slicing Technique in 3D Model
5.3.5 Printing Path Trajectory
5.3.6 Improvement in Computational Simulation
5.3.7 Improving Service-Oriented Architecture
5.3.8 Capabilities of Cloud Computing
5.3.9 Hamming Distance Technique
5.3.10 Improving Knowledge Skills
5.3.11 Object Detection Algorithm
5.3.12 Improvement in Manufacturing Defects
5.4 Conclusion
References
Chapter 6 Role of Cybersecurity to Safeguard 3D Bioprinting in Healthcare: Challenges and Opportunities
6.1 Introduction
6.2 Related Work
6.3 Creation of 3D Objects and Printing
6.3.1 Benefits of 3D Printing
6.3.2 Bioprinting
6.3.3 A Flow Diagram Depicting the Bioprinting Process
6.3.4 Datasets Used in Bioprinting
6.4 Schematic Diagram of 3D Bioprinting
6.4.1 3D Bioprinting Strategies
6.4.2 Comparison Among the 3D Bioprinting Approaches
6.4.3 Materials Used in Bioprinting
6.4.4 Bioprinting in Diverse Domains
6.5 Cyberthreats Posed to Bioprinting
6.5.1 Challenges and Opportunities of Cybersecurity in Bioprinting
6.5.2 Proposed Solutions
6.5.3 Combating the Cybersecurity Risks of 3D Bioprinting
6.5.4 Blockchain Technology and Bioprinting
6.5.5 A Comparative Survey of Cyberthreats in Additive Manufacturing Technology
6.6 Conclusion
References
Chapter 7 Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting
7.1 Introduction
7.2 Current 3D Bioprinting Market Trends
7.3 Legal and Ethical Perspectives
7.4 Regarding the Introduction and Advancement of 3D Bioprinting
7.4.1 Current and Potential Paths for Bioethical Discourse
7.4.2 Legal Concerns with the Introduction of 3D Bioprinting Into Clinical Practice
7.4.3 Ethical Concerns with the 3D Bioprinting of Artificial Ovaries and Their Use in Clinical Settings
7.5 Conclusion
7.6 Future Scope
References
Chapter 8 Optimizing 3D Bioprinting Using Advanced Deep Learning Techniques A Comparative Study of CNN, RNN, and GAN
8.1 Introduction
8.2 Convolutional Neural Networks in Optimization of 3D Bioprinting
8.3 RNN in Optimization of 3D Bioprinting
8.4 Generative Adversarial Networks (GAN) in Optimization of 3D Bioprinting
8.5 Datasets Used for Optimization of 3D Bioprinting
8.6 3D Slicer Medical Image Segmentation Dataset
8.7 Sensor Data
8.8 Open Organ Database Dataset
8.9 Proposed Model
8.10 CNN U-Net
8.11 RNN Long Short-Term Memory
8.12 Wasserstein Generative Adversarial Network
8.13 Process of Combined Model
8.14 Conclusion
References
Chapter 9 Research Trends in Intelligence-Based Bioprinting for Construction Engineering Applications
9.1 Introduction
9.2 Analysis of Bioprinting
9.3 Model Development in Bioprinting Technology
9.4 3D Bioprinting Academic Institutions in the World
9.5 Emerging Bioprinting Technology
9.5.1 Opportunities
9.5.2 Challenges
9.6 Development in Bioengineering
9.7 Evolution of Patent Trends in Bioprinting
9.8 Conclusions
References
Chapter 10 Design and Development to Collect and Analyze Data Using Bioprinting Software for Biotechnology Industry
10.1 Introduction
10.2 Digital Technology in Bioprinting
10.2.1 Shape of Bioprinting
10.2.2 Heterogeneity Units of Material
10.2.2.1 Tissue Improvement
10.2.2.2 Formation of Biomaterials
10.2.2.3 Biomaterial and Biological Factors
10.2.3 Dynamic Changes in Fabrication Process
10.3 Designing Techniques in Bioprinting
10.3.1 Data Processing in Biomedical Imaging
10.3.2 Process Bioprinting Techniques
10.3.3 Interaction of Bioink Formulation
10.4 3D Bioprinting
10.4.1 Optimized Bioprinting
10.4.2 Modifying Crosslinking
10.4.3 Multiple Crosslinking
10.4.4 Enhance Bioprinting
10.4.5 Hybrid Bioprinting
10.5 Enhanced Biotissue Printing
10.5.1 Integrating Thickness of Engineered Tissue
10.5.2 Integration and Enhancement of Cellular Interaction
10.5.3 DNA with a Smart Biomaterial
10.5.3.1 Biomaterials
10.5.3.2 Reactive Hydrogel to External Stimuli
10.5.4 Simulation
10.6 Conclusion
10.7 Future Work
References
Chapter 11 Cyborg Intelligence for Bioprinting in Computational Design and Analysis of Medical Application
11.1 Introduction
11.2 Next Generation of Bioprinting
11.2.1 Medicine Management
11.2.2 Varieties of Bioprinting Material
11.2.2.1 Thermoresponsive Materials
11.2.2.2 Biocompatible Polymers Materials
11.2.2.3 Endophyte Biocompatible Polymers Materials
11.2.2.4 Photo-Conductive Polymer Materials
11.2.2.5 UV-Assisted in 3D Printing
11.2.2.6 Sensitivity Polymeric Materials
11.2.2.7 Macromolecules Materials
11.2.2.8 Dual-Sensitive Materials
11.2.3 Biosensing Scaffolds
11.3 Biosensors and Actuators
11.3.1 Fabricated Scaffold Tissues
11.3.2 Vascularizing Tissues
11.3.3 4D Bioprinting Neural Tissue
11.3.4 Longitudinal Deformation
11.3.5 Uses of Biomedical Appliances
11.4 Enhancing Technology in Bioprinting
11.5 Conclusion and Future Work
References
Chapter 12 Computer Vision-Aides 3D Bioprinting in Ophthalmology Recent Trends and Advancements
12.1 Introduction
12.2 Digital Laser Printing Techniques
12.2.1 Tissue Engineering Industry
12.2.1.1 Printing Biomedical Structure
12.2.1.2 Electrochemical Bioprinter
12.2.1.3 Nozzle Free Printing
12.3 3D Printing Biological Material
12.3.1 Optical Quality of 3D Printing Technology
12.3.2 Repair Damage Tissue
12.3.3 Eye Blindness
12.3.4 Medicine Company
12.3.5 Artificial Prosthetic Eye
12.4 Conclusion and Future Work
References
Chapter 13 Intelligent Image Classification for 3D Printing in Industry 4.0
13.1 Introduction
13.2 Advantages
13.3 Methodology
13.4 3D Printing Technology
13.4.1 Automating Selection of Best Tasks
13.4.2 3D Printing Performances Analysis
13.4.3 Choose High-Performance Material
13.4.4 3D Printing Problem Solving
13.5 ANN Methods
13.6 Conclusions
References
Chapter 14 Bioprinting and Robotics Engineering: Applications, Recent Progress, and Future Directions
14.1 Introduction
14.2 Background
14.3 3D Printing
14.4 3D Printing Applications
14.4.1 Tooling for Prototyping and End-Use Parts
14.4.2 Industrial Uses of 3D Printing
14.4.2.1 Aviation
14.4.3 Which Are Some of the Most Typical Items Produced Using a 3D Printer?
14.4.4 Wing and Propellers
14.4.4.1 Role of 3D Printing Automobile Industry
14.4.4.2 Automobile Quality is Improved via Rapid Prototyping
14.4.4.3 Tool Customization
14.4.4.4 Trying to Lose the Most Weight Possible
14.5 Recent Progress in 3D Printing
14.5.1 Unmanned Aerial Vehicles (UAV)
14.5.2 Medicine
14.5.3 Organs and Tissues May Be Printed via Bioprinting
14.5.4 Smart Building
14.5.5 Sand Dunes
14.5.6 Metal Engraving
14.5.7 3D Printing's Use in Robot Construction
14.5.8 Characteristics
14.5.8.1 Robots Are Not Like Other Industrial Items
14.5.8.2 Design Versatility and Rapid Prototyping
14.5.8.3 Lower Output Figures
14.5.8.4 Robot Types and Applications
14.5.8.5 Independent
14.5.8.6 Submarine Robot
14.6 Future Directions in 3D Printing
14.6.1 Robots That Clean Solar Panels
14.6.2 Factory Robots
14.6.3 Case Study
14.6.4 Adding the Dataset to the Notebook
14.6.5 Basic Information
14.6.6 Inference from Heatmap
14.6.7 Inference from Model
14.6.8 The Coefficients Suggest the Following
14.7 Conclusion and Discussion
14.8 Future Scope
References
Chapter 15 3D Bioprinting Technology Optimization Using Machine Learning
15.1 Introduction
15.2 Human Organs Printed Through 3D Printers
15.2.1 Prioritize Cell Membrane
15.2.2 Prediction-Based Algorithms
15.2.3 Bioimaging through CAD Software
15.3 Predictive Trial and Error 3D Printing
15.4 Conclusions
References
Index
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