Authors
Jodie R Malcolm, Stuart Lacy, Andrea Papaleo, Le Liu, Suleiman H Kwairanga, Graeme Park, Joanne Marrison, Olympia Physouni, Richard Kasprowicz, Kerry Hallbrook, Richard Williams, Kildare Miranda, Teng-Leong Chew, Caron A Jacobs, Mahmoud Bukar Maina, Ana Paula C A Lima, Jeremy C Mottram, Beth A Cimini, Ben Powell, Peter O'Toole, Laura Wiggins, William J Brackenbury
Published in
Journal of cell science. Volume 139. Issue 21. Nov 01, 2026. Epub Aug 12, 2026.
Abstract
Global and community-driven initiatives have recently achieved considerable success in overcoming key challenges that hinder the widespread adoption of advanced microscopy and bioimage analysis tools in under-resourced settings. To build upon this progress, we held a workshop in May 2025 at the University of York, UK to address the needs and barriers associated with implementing time-lapse imaging and machine learning-based phenotyping in low-resource research environments. We focussed on identifying the specific challenges faced by the existing networks represented at the meeting, emphasising how integrating combined imaging hardware and machine learning-based approaches can solve these problems. This article summarises the key observations and actionable strategies made at the workshop. These proposed steps aim to significantly increase the dissemination and uptake of these powerful technologies to advance biological research in low-resource settings globally.
PMID:
42583731
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 5
- Comments 0