4 Artificial intelligence in bacterial staining and cell counting
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G Shree Kumari
Abstract
The integration of artificial intelligence (AI) with bacterial staining and cell counting represents a transformative advancement in microbiological research and clinical diagnostics. This chapter explores the novel methodologies that leverage machine learning and computer vision techniques to enhance and automate traditional staining protocols and cell enumeration processes. Key topics include the development of AI models that significantly improve the accuracy and efficiency of identifying bacterial morphologies and quantifying cellular populations using various staining techniques. The utilization of deep learning algorithms for image analysis is highlighted, showcasing their capability to reduce human error and processing time. Moreover, the chapter discusses the implications of these innovations in clinical microbiology, emphasizing how AI-driven systems can expedite diagnostics and enable real-time monitoring of microbial populations. By providing comprehensive insights into the intersection of AI with bacterial staining and cell counting, this chapter not only elucidates the current state of research but also projects future directions for technological advancements in microbiological applications. In conclusion, the chapter underscores the importance of interdisciplinary collaboration among microbiologists, data scientists, and healthcare professionals in harnessing AI to revolutionize microbial analysis.
Abstract
The integration of artificial intelligence (AI) with bacterial staining and cell counting represents a transformative advancement in microbiological research and clinical diagnostics. This chapter explores the novel methodologies that leverage machine learning and computer vision techniques to enhance and automate traditional staining protocols and cell enumeration processes. Key topics include the development of AI models that significantly improve the accuracy and efficiency of identifying bacterial morphologies and quantifying cellular populations using various staining techniques. The utilization of deep learning algorithms for image analysis is highlighted, showcasing their capability to reduce human error and processing time. Moreover, the chapter discusses the implications of these innovations in clinical microbiology, emphasizing how AI-driven systems can expedite diagnostics and enable real-time monitoring of microbial populations. By providing comprehensive insights into the intersection of AI with bacterial staining and cell counting, this chapter not only elucidates the current state of research but also projects future directions for technological advancements in microbiological applications. In conclusion, the chapter underscores the importance of interdisciplinary collaboration among microbiologists, data scientists, and healthcare professionals in harnessing AI to revolutionize microbial analysis.
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
Chapters in this book
- Frontmatter I
- Dedication V
- Preface VII
- Contents IX
- 1 Understanding artificial intelligence: an introduction, history, and foundations 1
- 2 Basics of machine learning (ML) and deep learning (DL), secondary data source and training, application and AI tools, challenges, and future perspectives of AI 25
- 3 Cellular image classification and identification of genetic variations using artificial intelligence 47
- 4 Artificial intelligence in bacterial staining and cell counting 65
- 5 Use of artificial intelligence in the prediction of microbial species 79
- 6 Transformative AI applications in environmental microbiology: pioneering research and sustainable solutions 97
- 7 AI in food production and processing: applications and challenges 125
- 8 Artificial intelligence in microbial food safety 153
- 9 AI in plant growth promotion and plant disease management 183
- 10 Role of artificial intelligence (AI) and machine learning (ML) in disease forecasting and disease epidemiology 207
- 11 Artificial intelligence in diagnostics 229
- 12 Artificial intelligence in bacterial culture plate images 263
- 13 Prediction of antimicrobial activity using artificial intelligence 281
- 14 Artificial intelligence and MALDI-TOF MS 313
- 15 Artificial intelligence in clinical microbiology: regeneration of diagnostics techniques using GANs and reinforcement learning for drug discovery and development in human welfare 337
- 16 Reimagining perfusion bioreactors with artificial intelligence 357
- Index 381
