Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging
-
K. Dhanasekaran
,
M. Anand
Abstract
Recent advances in biomedical engineeringbiomedical engineering show that quantum dots (QDs) are powerful imaging agents that exhibit desirable characteristics like size-tunable emission and photostability. In this chapter we review the synthesis of QDs and their surface functionalization, with an emphasis on their employment in state-of-the-art imaging systems. Of course, there are discussion of the effects of QD size and the surface structure on optical characteristics, advantages and disadvantages of various synthesis methodologies, and the difficulties that QD production poses for cost reduction and manufacturability. In addition to providing an overview of the synthesis of QDs andQDs, functionalization of QDs for biomedical applications, the chapter also discusses recent developments in the area of surface coatings in QD that improve their biocompatibility and specificity in medical applications. This chapter seeks to map the existing and future agenda for QD technology by analyzing the possible developments and limitations as of today and suggest future research questions that enable the practical implementation of this technology. Major topics covered include synthesis of QDs and their surface chemistry, optical characteristics, and functionalization of the particles; and the issues of cost and practical bottlenecks to the commercial exploitation of the materialmaterial.
Abstract
Recent advances in biomedical engineeringbiomedical engineering show that quantum dots (QDs) are powerful imaging agents that exhibit desirable characteristics like size-tunable emission and photostability. In this chapter we review the synthesis of QDs and their surface functionalization, with an emphasis on their employment in state-of-the-art imaging systems. Of course, there are discussion of the effects of QD size and the surface structure on optical characteristics, advantages and disadvantages of various synthesis methodologies, and the difficulties that QD production poses for cost reduction and manufacturability. In addition to providing an overview of the synthesis of QDs andQDs, functionalization of QDs for biomedical applications, the chapter also discusses recent developments in the area of surface coatings in QD that improve their biocompatibility and specificity in medical applications. This chapter seeks to map the existing and future agenda for QD technology by analyzing the possible developments and limitations as of today and suggest future research questions that enable the practical implementation of this technology. Major topics covered include synthesis of QDs and their surface chemistry, optical characteristics, and functionalization of the particles; and the issues of cost and practical bottlenecks to the commercial exploitation of the materialmaterial.
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
