Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications
-
D.M. Gokul Varshan
,
Swati Mutha
Abstract
Nanofibers, known as electrospun, have brightened various sections of biomedical applications, especially the areas of drug delivery and tissue regenerationtissue regeneration. This chapter also analyzed advanced applications and progress in electrospun nanofibers; this elucidates the additive impact the nanofibers make to the characteristics and performance improvements through various techniques. Advances in electrospinning, such as coaxial and multineedle systems, have made it possible to produce nanofibers with customized properties for particular medicinal uses. Their applicability has been further increased by functionalization techniques such as chemical, physical, and biological alterations, which have made it easier to create nanofibers with improved bioactivity and performance. Key applications that are being addressed are tissue engineering, where nanofiber scaffolds assist stem cell differentiation and tissue regeneration, and targeted drug delivery systems for cancer therapy, where nanofibers offer controlled release and lower systemic toxicity. The application of biodegradable nanofibers in wound healing is also covered in this chapter, with an emphasis on how to reduce scarring and encourage recovery without scars. Future directions to improve the functionality and design of nanofibernanofibers include the incorporation of machine learning techniques. This thorough analysis highlights the potential of electrospun nanofibers to transform healthcare by offering insightful information on their present and possible future uses in medicine.
Abstract
Nanofibers, known as electrospun, have brightened various sections of biomedical applications, especially the areas of drug delivery and tissue regenerationtissue regeneration. This chapter also analyzed advanced applications and progress in electrospun nanofibers; this elucidates the additive impact the nanofibers make to the characteristics and performance improvements through various techniques. Advances in electrospinning, such as coaxial and multineedle systems, have made it possible to produce nanofibers with customized properties for particular medicinal uses. Their applicability has been further increased by functionalization techniques such as chemical, physical, and biological alterations, which have made it easier to create nanofibers with improved bioactivity and performance. Key applications that are being addressed are tissue engineering, where nanofiber scaffolds assist stem cell differentiation and tissue regeneration, and targeted drug delivery systems for cancer therapy, where nanofibers offer controlled release and lower systemic toxicity. The application of biodegradable nanofibers in wound healing is also covered in this chapter, with an emphasis on how to reduce scarring and encourage recovery without scars. Future directions to improve the functionality and design of nanofibernanofibers include the incorporation of machine learning techniques. This thorough analysis highlights the potential of electrospun nanofibers to transform healthcare by offering insightful information on their present and possible future uses in medicine.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
