Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds
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D.M. Gokul Varshan
Abstract
Tissue engineeringtissue engineering has embraced the development of biodegradable scaffolds based on correct predictions of the long-term stability of the scaffolds. A literature review reveals that the conventional experimental approaches do not offer exhaustive information about scaffold degradation over a long-term time frame. This chapter examines the changes that utilizing predictive machine learning models bring to the evaluation of scaffold biodegradability stability by incorporating multiple data sources and analyzing the data using more sophisticated methods. Specific factors include the quality of data, the use of longitudinal datalongitudinal data to capture temporal aspects of degradation, and how this quality affects the models. The chapter also describes the latest approaches and applications to data cleansing and preparation, and data fusion techniques, while critiquing and comparing them based on their impact on model quality and resilience. In relation to the present topic of this chapter, current developments and trends in the use of PM for scaffold design and research limitations are outlined comprehensively to give a framework regarding the possibilities of PM in biomaterial scaffold design and clinical outcome enhancement.
Abstract
Tissue engineeringtissue engineering has embraced the development of biodegradable scaffolds based on correct predictions of the long-term stability of the scaffolds. A literature review reveals that the conventional experimental approaches do not offer exhaustive information about scaffold degradation over a long-term time frame. This chapter examines the changes that utilizing predictive machine learning models bring to the evaluation of scaffold biodegradability stability by incorporating multiple data sources and analyzing the data using more sophisticated methods. Specific factors include the quality of data, the use of longitudinal datalongitudinal data to capture temporal aspects of degradation, and how this quality affects the models. The chapter also describes the latest approaches and applications to data cleansing and preparation, and data fusion techniques, while critiquing and comparing them based on their impact on model quality and resilience. In relation to the present topic of this chapter, current developments and trends in the use of PM for scaffold design and research limitations are outlined comprehensively to give a framework regarding the possibilities of PM in biomaterial scaffold design and clinical outcome enhancement.
Kapitel in diesem Buch
- 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
Kapitel in diesem Buch
- 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
