Automated materials characterization using machine learning for screening biocompatible materials
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V. Vaishnavi
Abstract
Currently, biomedical engineeringbiomedical engineering has received a boost, largely due to the rate at which new biocompatible materials have been developed, offering innovative solutions to many biomedical application areas. This chapter focuses on the constantly growing field of biocompatible materials, characterized by the high relevance of these materials in enhancing patients’ qualitypatients’ quality of life and driving new developments in medicine. Understanding the surface characteristics, chemical properties, mechanical properties, and biological properties is beneficial in guiding the choice of materials for better clinical applications. This discussion extends to current developments in biocompatible materials, which have concentrated on advancements in several new synthetic polymers, new metals, and composite materials. It also addresses other areas of material characterization, presenting challenges such as ethical concerns and compliance with legislation, including the need to continually subject materials to rigorous testing and ethical standards. This chapter provides a comprehensive and critical review of recent advancements and prospective developments in biocompatible materialsbiocompatible materials and is intended to serve as a useful reference for engineers working in the biomaterials and biomedical fields for their material selection and application.
Abstract
Currently, biomedical engineeringbiomedical engineering has received a boost, largely due to the rate at which new biocompatible materials have been developed, offering innovative solutions to many biomedical application areas. This chapter focuses on the constantly growing field of biocompatible materials, characterized by the high relevance of these materials in enhancing patients’ qualitypatients’ quality of life and driving new developments in medicine. Understanding the surface characteristics, chemical properties, mechanical properties, and biological properties is beneficial in guiding the choice of materials for better clinical applications. This discussion extends to current developments in biocompatible materials, which have concentrated on advancements in several new synthetic polymers, new metals, and composite materials. It also addresses other areas of material characterization, presenting challenges such as ethical concerns and compliance with legislation, including the need to continually subject materials to rigorous testing and ethical standards. This chapter provides a comprehensive and critical review of recent advancements and prospective developments in biocompatible materialsbiocompatible materials and is intended to serve as a useful reference for engineers working in the biomaterials and biomedical fields for their material selection and application.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
