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Advancements in the Implementation of Artificial Intelligence for Imaging and Image Analysis
Mariana De Niz1,2, David Kirchenbuechler1,2, Peter Dluhy1,2
1Center for Advanced Microscopy, Feinberg School of Medicine, Northwestern University, 320 East Superior St, Chicago, IL 60611, USA.
Summary
Artificial intelligence (AI) is revolutionizing biological and biomedical research, particularly in microscopy. This technology streamlines the entire imaging workflow, from experiment design to data sharing, addressing challenges posed by complex data volumes.
Area of Science:
- Biological and biomedical research
- Microscopy imaging
Background:
- Artificial intelligence (AI) has gained significant momentum in life sciences.
- Classical methods struggle with the increasing data complexity from modern microscopes.
Purpose of the Study:
- To discuss the implementation of AI across various stages of biological imaging experiments.
- To highlight the advantages and challenges of using AI in microscopy workflows.
Main Methods:
- Review of AI applications in experimental design.
- Analysis of AI integration in data acquisition and processing.
- Examination of AI's role in data dissemination and analysis.
Main Results:
- AI enhances efficiency and capability across the entire imaging pipeline.
- Successful AI integration is observed from initial experimental planning to final data sharing.
- Specific advantages and challenges are identified at each step of AI implementation.
Conclusions:
- AI is a transformative tool for modern biological and biomedical imaging.
- Addressing the challenges of AI integration is crucial for maximizing its benefits.
- AI adoption is essential for managing the complexity and volume of data in advanced microscopy.
