Computational Intelligence in Bioprinting : Challenges and Future Directions

• Autore: Gangadevi E
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2024; ©2024
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (346 pages)
• Disciplina: 610.285
• Soggetto topico: Computational intelligence; Tissue engineering
• Altri autori: ShriM. Lawanya BalusamyBalamurugan
• Nota di contenuto: 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.
• Sommario/riassunto: This book delves into the intersection of computational intelligence and bioprinting, exploring their applications in medicine, tissue engineering, and regenerative medicine. It discusses the design and evaluation of bioprinting technologies, the integration of IoT devices, and the use of AI-based AR/VR models in biomedical industries. The book also addresses cybersecurity challenges in 3D bioprinting, legal and bioethical considerations, and optimization techniques using deep learning. Aimed at professionals and researchers in biomedical engineering and computational sciences, it provides insights into the future opportunities and challenges of these technologies.
• Titolo autorizzato: Computational Intelligence in Bioprinting
• ISBN: 9781394204878; 1394204876; 9781394204861; 1394204868
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911019590603321
• Lo trovi qui: Univ. Federico II