Removing barriers to advanced imaging and machine learning-based analysis

Area of Science:

• Biotechnology
• Microscopy
• Machine Learning in Biology

Background:

• Global initiatives are improving access to advanced microscopy and bioimage analysis in under-resourced areas.
• Challenges remain in adopting these tools, particularly time-lapse imaging and machine learning-based phenotyping, in low-resource research environments.

Purpose of the Study:

• To address the needs and barriers for implementing time-lapse imaging and machine learning-based phenotyping in low-resource settings.
• To identify specific challenges faced by existing research networks.
• To emphasize how integrated imaging hardware and machine learning can solve these problems.

Main Methods:

• A workshop was convened in May 2025 at the University of York, UK.
• Discussions focused on the challenges and needs of researchers in low-resource settings.
• Key observations and actionable strategies were identified and summarized.

Main Results:

• Specific challenges faced by researchers in under-resourced settings were identified.
• The potential of integrated imaging hardware and machine learning approaches to overcome these challenges was highlighted.
• Actionable strategies for dissemination and uptake were proposed.

Conclusions:

• The workshop provided key insights and strategies to overcome barriers in adopting advanced imaging and machine learning for biological research.
• Implementing these strategies can significantly increase the use of powerful technologies in low-resource settings globally.
• This advancement will foster biological research progress worldwide.