Smart Microscopy, AI, and Robotics
Automated Future Disease Detection
- 1st Edition - May 1, 2027
- Latest edition
- Editors: Priyadarshini Pattanaik, Chiranji Lal Chowdhary, Gaurav Agarwal
- Language: English
Smart Microscopy, AI, and Robotics: Automated Future Disease Detection addresses the need to combine automated microscopy with artificial intelligence and robotics to improv… Read more
Description
Description
Smart Microscopy, AI, and Robotics: Automated Future Disease Detection addresses the need to combine automated microscopy with artificial intelligence and robotics to improve accuracy, throughput, and safety in disease detection. It surveys methods for image acquisition, processing, segmentation, and interpretation across sample types, including blood smears, tissue biopsies, cytology, and cytopathology, equipping researchers, clinicians, and technologists with actionable knowledge for deploying AI-powered diagnostic pipelines.
The book covers foundational principles and advanced applications, beginning with microscopy and contrast-enhanced imaging, followed by automated microscope–robotics integration, image acquisition and analysis pipelines, and AI methods for disease detection. It then explores applications such as blood disorders, infectious diseases, and cancer diagnostics, including AI-based analysis of neurological and cellular imaging, advanced deep-learning architectures, and foundation models for smart microscopy. System-level considerations address performance metrics, explainability, bias mitigation, ethics, clinical governance, regulation, safety, and human–AI interaction and interoperability. The book also examines AI-enabled drug discovery, therapeutic target identification, smart laboratories, automated diagnostics, and global health impact.
Readers will gain practical guidance on building high-throughput, AI-assisted diagnostic systems that are robust, interpretable, and scalable, with insights into data annotation, model validation, and integrating AI with robotic microscopy in clinical and research settings. The work anticipates developments such as self-calibrating instruments, autonomous laboratory workflows, and digital pathology ecosystems.
The book covers foundational principles and advanced applications, beginning with microscopy and contrast-enhanced imaging, followed by automated microscope–robotics integration, image acquisition and analysis pipelines, and AI methods for disease detection. It then explores applications such as blood disorders, infectious diseases, and cancer diagnostics, including AI-based analysis of neurological and cellular imaging, advanced deep-learning architectures, and foundation models for smart microscopy. System-level considerations address performance metrics, explainability, bias mitigation, ethics, clinical governance, regulation, safety, and human–AI interaction and interoperability. The book also examines AI-enabled drug discovery, therapeutic target identification, smart laboratories, automated diagnostics, and global health impact.
Readers will gain practical guidance on building high-throughput, AI-assisted diagnostic systems that are robust, interpretable, and scalable, with insights into data annotation, model validation, and integrating AI with robotic microscopy in clinical and research settings. The work anticipates developments such as self-calibrating instruments, autonomous laboratory workflows, and digital pathology ecosystems.
Key features
Key features
- Integrates AI and robotics for automated microscopic diagnostics
- Presents AI-driven detection of diseases from blood/tibial samples and slides
- Details robotics-enabled slide handling, imaging, and workflow automation
- Addresses ethics, regulation, and human–AI collaboration in diagnostics
Readership
Readership
Researchers, graduate students and professionals in biomedical imaging, diagnostic imaging, and biomedical engineering
Table of contents
Table of contents
1. Introduction to Microscopy and enhanced cell analysis in contrast microscopy images.
2. Study of Automated Microscopic Diagnosis with machine-microscope integration in imaging systems
3. Image Acquisition, Image Segmentation, Image Classification, and Image Analysis and Preprocessing Methods.
4. Robotics in Microscopy: Automated Slide Handling, Scanning, and Imaging Workflow Automation
5. Role of big data and statistical analysis for handling large medical datasets of microscopic images.
6. Role of Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) for automated disease detection.
7. AI-Based Detection of Blood Disorders and Infectious Diseases: Malaria, Leukemia, TB, and Beyond
8. Robotics-AI Integration for automated disease detection for early and accurate treatment.
9. The use of AI for automated Histopathology and cytopathology analysis.
10. Performance measurements in system validation, clinical settings, and workflow integration.
11. Issues related to ethics, regulation, and safety considerations of AI in healthcare
12. Production and development of novel, precise drug discovery and therapeutic targets through AI from microscopic disease insights.
13. Potential Future Scope of AI in smart labs, automated diagnostics, and global health impact.
14. Explainable and Interpretable AI for Microscopy: Transparency, Bias Mitigation, and Trust in Automated Diagnostics
15. Human–AI Interaction, Clinical Adoption, and Interoperability Standards in Smart Microscopy Systems
2. Study of Automated Microscopic Diagnosis with machine-microscope integration in imaging systems
3. Image Acquisition, Image Segmentation, Image Classification, and Image Analysis and Preprocessing Methods.
4. Robotics in Microscopy: Automated Slide Handling, Scanning, and Imaging Workflow Automation
5. Role of big data and statistical analysis for handling large medical datasets of microscopic images.
6. Role of Artificial Intelligence (AI), Deep Learning (DL), and Machine Learning (ML) for automated disease detection.
7. AI-Based Detection of Blood Disorders and Infectious Diseases: Malaria, Leukemia, TB, and Beyond
8. Robotics-AI Integration for automated disease detection for early and accurate treatment.
9. The use of AI for automated Histopathology and cytopathology analysis.
10. Performance measurements in system validation, clinical settings, and workflow integration.
11. Issues related to ethics, regulation, and safety considerations of AI in healthcare
12. Production and development of novel, precise drug discovery and therapeutic targets through AI from microscopic disease insights.
13. Potential Future Scope of AI in smart labs, automated diagnostics, and global health impact.
14. Explainable and Interpretable AI for Microscopy: Transparency, Bias Mitigation, and Trust in Automated Diagnostics
15. Human–AI Interaction, Clinical Adoption, and Interoperability Standards in Smart Microscopy Systems
Product details
Product details
- Edition: 1
- Latest edition
- Published: May 1, 2027
- Language: English
About the editors
About the editors
PP
Priyadarshini Pattanaik
Priyadarshini Pattanaik is a Senior Lecturer at the Berlin School of Business and Innovation, Germany. Her research focuses on quantitative analysis and the development of machine learning algorithms using deep neural networks and graphical models for visual computing, with applications in medical image analysis and disease detection.
She previously served as a Postdoctoral Scientist at IMT Atlantique, France, within the Image and Information Processing department of the LaTIM research group (2020–2022). Her work addressed key challenges in the musculoskeletal (MSK) field, particularly exploring the relationship between joint shape and function through advanced computational methods. Earlier, she worked as a Postdoctoral Fellow collaborating with several academic and industry partners, including Télécom SudParis, Université Paris-Saclay, the Centre for Mathematical Morphology at Mines ParisTech, and the company TRIBVN (2019). She has authored numerous publications in high-impact SCI and Scopus-indexed journals and international conferences.
Affiliations and expertise
Senior Lecturer, Berlin School of Business and Innovation, GermanyCC
Chiranji Lal Chowdhary
Dr. Chiranji Lal Chowdhary is Professor in the School of Computer Science Engineering and Information Systems at Vellore Institute of Technology, India. He has more than 20 years of academic and research experience in artificial intelligence, machine learning, deep learning, computer vision and intelligent systems. His research spans industrial AI, Industry 4.0 technologies, smart healthcare and data-driven decision-making systems. He has published extensively in international journals and conferences and has edited several scholarly volumes. He has supervised doctoral and postgraduate researchers and contributes actively to research evaluation, curriculum development and academic leadership. His expertise in intelligent systems and digital transformation aligns closely with the themes of AI-enabled industrial ecosystems and lifecycle intelligence explored in this book.
Affiliations and expertise
Professor, Vellore Institute of Technology, Vellore, IndiaGA
Gaurav Agarwal
Dr. Gaurav Agarwal is Professor and Program Co-Chair (B.Tech CSE) at Galgotias University, Greater Noida, India. He holds a Ph.D. in Computer Science and Engineering from IIT (ISM) Dhanbad and brings over 21 years of academic and leadership experience, including roles as Associate Head, Academic Head, and Central NBA Coordinator (2018–2022). His research spans speech signal processing, genetic algorithms, and web search engines. He has published over 40 papers in reputed journals and conferences and holds eight Indian patents (six granted). His honors include the IRNet Young Investigator Award and recognitions from Rotary International.
Affiliations and expertise
Professor, Galgotias University, Greater Noida, India