John Wiley & Sons Computational Intelligence in Bioprinting Cover COMPUTATIONAL INTELLIGENCE IN BIOPRINTING The book provides a comprehensive exploration of the evol.. Product #: 978-1-394-20439-7 Regular price: $167.29 $167.29 Auf Lager

Computational Intelligence in Bioprinting

Challenges and Future Directions

Gangadevi, E. / Shri, M. Lawanya / Dhanaraj, Rajesh Kumar / Balusamy, Balamurugan (Herausgeber)

Cover

1. Auflage März 2024
352 Seiten, Hardcover
Fachbuch

ISBN: 978-1-394-20439-7
John Wiley & Sons

Weitere Versionen

epubpdf

COMPUTATIONAL INTELLIGENCE IN BIOPRINTING

The book provides a comprehensive exploration of the evolving field of bioprinting in regenerative medicine and is an essential guide for professionals seeking a thorough understanding of the field.

Computational Intelligence in Bioprinting provides a comprehensive overview of the evolving field of bioprinting in reformative medicine, defining the process of printing structures using viable cells, biomaterials, and living molecules. The primary goal is to provide substitutes for tissue implants, which might lead to eliminating the requirement for organ donors, as well as to transform animal testing for the learning and analysis of disease and the growth of treatments. The book offers a comprehensive overview of bioprinting technologies and their applications, emphasizing the integration of computation intelligence, artificial intelligence, and other computer science advancements in the field. By harnessing the power of computational intelligence techniques such as AI, machine learning, optimization algorithms, and data analytics, existing hurdles can be overcome and the full potential of bioprinting can be unlocked.

The book covers an extensive range of topics, including bio-ink formulation and characterization, bioprinter hardware and software design, tissue and organ modeling, image analysis, process optimization, and quality control.

Audience

The book is aimed at professionals, practitioners and researchers in the fields of bioprinting, tissue engineering, and computational intelligence in medicine.

Preface xv

1 The Emergence of Bioprinting and Computational Intelligence 1
P.M. Kavitha, S. Jayachandran and M. Anitha

1.1 Introduction 2

1.2 Related Study 3

1.3 Understanding the Basics of Bioprinting and Computational Intelligence 6

1.4 The Role of Computational Intelligence in Bioprinting 8

1.5 Applications of Bioprinting and Computational Intelligence in Medicine 9

1.6 Bioprinting and Computational Intelligence in Tissue Engineering and Regenerative Medicine 10

1.7 Advancements in Bioprinting and Computational Intelligence Technologies 12

1.8 The Ethical and Regulatory Implications of Bioprinting and Computational Intelligence 13

1.9 The Future of Bioprinting and Computational Intelligence: Opportunities and Challenges 14

1.10 Case Studies: Bioprinting and Computational Intelligence in Action 15

1.11 Conclusion 19

2 Design, Architecture, Implementation, and Evaluation of Bioprinting Technology for Tissue Engineering 21
Vimala R. T. V., Gangadevi E. and Lawanya Shri M.

2.1 Introduction 21

2.2 3D Bioprinting 23

2.3 Material Characteristics 24

2.4 Mechanical Properties 25

2.5 Biomaterials 25

2.6 Design, Architecture of 3D Bioprinting 25

2.7 3D Bioprinting Tissue Models 28

2.8 3D Multimaterial Bioprinting-Development of Complex Architectures 29

2.9 Implementation and Evaluation 29

2.10 Bone 29

2.11 Cartilage 31

2.12 Soft Tissue Engineering 31

2.13 Vascular Tissue 32

2.14 Skin 32

2.15 Biocompatibility and Control of Degradation and Byproducts 33

2.16 Conclusion 33

3 Design and Development of IoT Devices: Methods, Tools and Technologies 39
Akash Kumar, Sachin Abhay Kumar, Richa Singh, Shivam Maloo, Dishant Rathi and K. Santhi

3.1 Introduction to IoT Devices and 3D Bioprinting 40

3.2 Methodology for Designing IoT Devices for 3D Bioprinting 40

3.3 Additional Considerations in IoT Device Design for 3D Bioprinting 42

3.4 Tools for Developing IoT Devices for 3D Bioprinting 44

3.5 Techniques for Developing IoT Devices for 3D Bioprinting 46

3.6 Case Studies of IoT Devices for 3D Bioprinting 49

3.7 Future Directions in IoT Devices for 3D Bioprinting 49

3.8 Conclusion 50

4 AI-Based AR/VR Models in Biomedical Sustainable Industry 4.0 53
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Rudra Pratap Ojha, Balamurugan Balusamy and Gangadevi E.

4.1 Introduction 54

4.2 Mixed Augmented Reality 56

4.3 AR Technology 64

4.4 Requirement of Augmented Reality 71

4.5 Conclusions 74

5 Computational Intelligence--Based Image Classification for 3D Printing: Issues and Challenges 79
Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, B. Tirapathi Reddy and Gangadevi E.

5.1 Introduction 80

5.2 Brief Concepts 81

5.3 Role of Artificial Intelligence in Industry 4.0 82

5.4 Conclusion 89

6 Role of Cybersecurity to Safeguard 3D Bioprinting in Healthcare: Challenges and Opportunities 93
Venkatalakshmi S.

6.1 Introduction 94

6.2 Related Work 95

6.3 Creation of 3D Objects and Printing 97

6.4 Schematic Diagram of 3D Bioprinting 100

6.5 Cyberthreats Posed to Bioprinting 106

6.6 Conclusion 125

7 Legal and Bioethical View of Educational Sectors and Industrial Areas of 3D Bioprinting 127
Pothys varan S., Balachander S. and Ashwini S.

7.1 Introduction 128

7.2 Current 3D Bioprinting Market Trends 130

7.3 Legal and Ethical Perspectives 135

7.4 Regarding the Introduction and Advancement of 3D Bioprinting 138

7.5 Conclusion 149

7.6 Future Scope 149

8 Optimizing 3D Bioprinting Using Advanced Deep Learning Techniques A Comparative Study of CNN, RNN, and GAN 157
K. Sujigarasharma, Sharulatha S., Lawanya Shri M., Gangadevi E. and Rajesh Kumar Dhanaraj

8.1 Introduction 158

8.2 Convolutional Neural Networks in Optimization of 3D Bioprinting 160

8.3 RNN in Optimization of 3D Bioprinting 160

8.4 Generative Adversarial Networks (GAN) in Optimization of 3D Bioprinting 161

8.5 Datasets Used for Optimization of 3D Bioprinting 162

8.6 3D Slicer Medical Image Segmentation Dataset 163

8.7 Sensor Data 163

8.8 Open Organ Database Dataset 164

8.9 Proposed Model 164

8.10 CNN U-Net 166

8.11 RNN Long Short-Term Memory 167

8.12 Wasserstein Generative Adversarial Network 168

8.13 Process of Combined Model 169

8.14 Conclusion 171

9 Research Trends in Intelligence-Based Bioprinting for Construction Engineering Applications 175
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Om Prakash, Balamurugan Balusamy and Feslin Anish Mon

9.1 Introduction 176

9.2 Analysis of Bioprinting 177

9.3 Model Development in Bioprinting Technology 179

9.4 3D Bioprinting Academic Institutions in the World 183

9.5 Emerging Bioprinting Technology 185

9.5.1 Opportunities 185

9.5.2 Challenges 186

9.6 Development in Bioengineering 186

9.7 Evolution of Patent Trends in Bioprinting 188

9.8 Conclusions 189

10 Design and Development to Collect and Analyze Data Using Bioprinting Software for Biotechnology Industry 193
Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Sanjay Kumar, Balamurugan Balusamy and Lakshmana Kumar Ramasamy

10.1 Introduction 194

10.2 Digital Technology in Bioprinting 195

10.3 Designing Techniques in Bioprinting 199

10.4 3D Bioprinting 201

10.5 Enhanced Biotissue Printing 203

10.6 Conclusion 205

10.7 Future Work 206

11 Cyborg Intelligence for Bioprinting in Computational Design and Analysis of Medical Application 211
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Naresh Kumar, Balamurugan Balusamy and Gangadevi E.

11.1 Introduction 212

11.2 Next Generation of Bioprinting 214

11.3 Biosensors and Actuators 223

11.4 Enhancing Technology in Bioprinting 232

11.5 Conclusion and Future Work 233

12 Computer Vision-Aides 3D Bioprinting in Ophthalmology Recent Trends and Advancements 239
Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, Ankita Tiwari, Balamurugan Balusamy and R. Gopal

12.1 Introduction 240

12.2 Digital Laser Printing Techniques 242

12.3 3D Printing Biological Material 248

12.4 Conclusion and Future Work 254

13 Intelligent Image Classification for 3D Printing in Industry 4.0 259
Rajbala, Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Gangadevi E. and Balamurugan Balusamy

13.1 Introduction 260

13.2 Advantages 261

13.3 Methodology 262

13.4 3D Printing Technology 262

13.5 ANN Methods 270

13.6 Conclusions 271

14 Bioprinting and Robotics Engineering: Applications, Recent Progress, and Future Directions 275
Pawan Whig, Shama Kouser, Ashima Bhatnagar Bhatia, Rahul Reddy Nadikattu and Yusuf Jibrin Alkali

14.1 Introduction 276

14.2 Background 277

14.3 3D Printing 279

14.4 3D Printing Applications 280

14.5 Recent Progress in 3D Printing 284

14.6 Future Directions in 3D Printing 289

14.7 Conclusion and Discussion 297

14.8 Future Scope 298

15 3D Bioprinting Technology Optimization Using Machine Learning 303
Kuldeep Singh Kaswan, Jagjit Singh Dhatterwal, Reenu Batra, Balamurugan Balusamy and Gangadevi E.

15.1 Introduction 304

15.2 Human Organs Printed Through 3D Printers 306

15.3 Predictive Trial and Error 3D Printing 317

15.4 Conclusions 318

References 319

Index 323
E. Gangadevi, is an assistant professor in the Department of Computer Science at Loyola College in Chennai, India. She has published two patents, authored / edited two books, more than 20 research papers in international journals and many book chapters.

M. Lawanya Shri, PhD, is an associate professor at the School of Information Technology and Engineering, VIT, Vellore, India. She has two patents, more than 50 articles in refereed journals and international conferences, and contributed many chapters to books.

Rajesh Kumar Dhanaraj, PhD, is a professor at the School of Computing Science and Engineering at Galgotias University in India. He has authored/edited more than 25 books on various technologies, 21 patents, and 50+ articles and papers in various refereed journals and international conferences.

Balamurugan Balusamy, PhD, is an associate dean to students at Shiv Nadar University at the Delhi-NCR Campus in Noida, India. He has authored/edited more than 80 books as well as over 200 contributions to international journals and conferences.

E. Gangadevi, Loyola College in Chennai, India; M. L. Shri, School of Information Technology and Engineering, VIT, Vellore, India; R. K. Dhanaraj, Galgotias University, Greater Noida, India; B. Balusamy, Shiv Nadar University, India