12 Artificial intelligence in bacterial culture plate images
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Pratishtha Jain
Abstract
Artificial intelligence (AI) is transforming microbiological analysis, particularly the interpretation of bacterial culture plate images. Traditional methods for identifying and quantifying bacterial colonies are laborious, time-consuming, and susceptible to human error. AI-powered image recognition systems offer a significant advantage by automating these tasks with greater speed, accuracy, and consistency. By recognizing subtle variations in colony morphology – shape, size, color, and texture – machine learning algorithms enable rapid and precise bacterial species classification. This capability accelerates diagnosis and enhances the accuracy of microbiological analysis across pharmaceutical research, clinical diagnosticsclinical diagnostics, and food safety monitoring. When integrated with high-throughput screeninghigh-throughput screening, AI efficiently processes large datasets, significantly streamlining laboratory workflows. Furthermore, advancements in deep learning models are driving predictive diagnostics by identifying patterns in colony formation potentially linked to specific pathogens or antibiotic resistance. As AI technology in microbial imagingmicrobial imaging continues to evolve, its role is expected to expand, facilitating faster, more accurate diagnoses and accelerating microbiology research. An important step toward automated, data-driven solutions for rapid and accurate diagnosis of infectious diseases has been made with the application of AI to bacterial culture plate analysis. The real-time analytical capabilities of AI algorithms can also overcome limitations of human expertise, unlocking new avenues for research. This chapter will explore cutting-edge AI algorithms and their applications in bacterial culture plate image analysis, while adhering to ethical and responsible AI principles.
Abstract
Artificial intelligence (AI) is transforming microbiological analysis, particularly the interpretation of bacterial culture plate images. Traditional methods for identifying and quantifying bacterial colonies are laborious, time-consuming, and susceptible to human error. AI-powered image recognition systems offer a significant advantage by automating these tasks with greater speed, accuracy, and consistency. By recognizing subtle variations in colony morphology – shape, size, color, and texture – machine learning algorithms enable rapid and precise bacterial species classification. This capability accelerates diagnosis and enhances the accuracy of microbiological analysis across pharmaceutical research, clinical diagnosticsclinical diagnostics, and food safety monitoring. When integrated with high-throughput screeninghigh-throughput screening, AI efficiently processes large datasets, significantly streamlining laboratory workflows. Furthermore, advancements in deep learning models are driving predictive diagnostics by identifying patterns in colony formation potentially linked to specific pathogens or antibiotic resistance. As AI technology in microbial imagingmicrobial imaging continues to evolve, its role is expected to expand, facilitating faster, more accurate diagnoses and accelerating microbiology research. An important step toward automated, data-driven solutions for rapid and accurate diagnosis of infectious diseases has been made with the application of AI to bacterial culture plate analysis. The real-time analytical capabilities of AI algorithms can also overcome limitations of human expertise, unlocking new avenues for research. This chapter will explore cutting-edge AI algorithms and their applications in bacterial culture plate image analysis, while adhering to ethical and responsible AI principles.
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
