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It addresses barriers that limit adoption of advanced microscopy and bioimage analysis tools, especially in under-resourced and low- and middle-income countries. The discussion highlights practical ways to integrate imaging hardware with machine learning approaches, improve dissemination and uptake, and expand access to powerful technologies to strengthen biological research 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Stuart Lacy3 , Andrea Papaleo4 , Le Liu4 , Suleiman H Kwairanga5,6 , Graeme Park7 , Joanne Marrison7 , Olympia Physouni2 , Richard Kasprowicz8 , Kerry Hallbrook9 , Richard Williams10 , Kildare Miranda11 , Teng-Leong Chew12 , Caron A Jacobs 13 , Mahmoud Bukar Maina5,6 , Ana Paula C A Lima11 , Jeremy C Mottram2 , Beth A Cimini4 , Ben Powell14 , Peter O’Toole7 , Laura Wiggins 15 , William J Brackenbury 1,2*  \n1 Jack Birch Cancer Research Unit, Department of Biology, University of York, Heslington, York, UK  \n2 York Biomedical Research Institute, Department of Biology, University of York, Heslington, York, UK  \n3 IT Services, University of York, Heslington, York, UK  \n4 Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA  \n5 Biomedical Science Research and Training Centre, Yobe State University, Damaturu,  \nNigeria  \n6 Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton, UK  \n7 Bioscience Technology Facility, Department of Biology, University of York, Heslington, York, YO10 5DD, UK  \n8 GlaxoSmithKline Medicines Research Centre, Stevenage, UK  \n9 UK Cell Culture and Banking, Discovery Sciences, AstraZeneca, Cambridge, UK  \n10 ioLight, Whitchurch, Hampshire, UK  \n11 Instituto de Biofisica Carlos Chagas Filho, Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil  \n12 Advanced Imaging Center and Integrative Imaging, Howard Hughes Medical Institute  \nJanelia Research Campus, Ashburn, VA, USA  \n13 Department of Pathology and Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa  \n14 Department of Mathematics, University of York, Heslington, York, UK  \n15 School of Chemical, Materials and Biological Engineering, University of Sheffield, Sheffield, UK  \n* Corresponding author.  \nSummary statement:  \nThis Perspective article conveys insights from a 2025 workshop on advancing timelapse imaging and machine learning-based phenotyping, outlining practical strategies to expand access to cutting-edge bioimaging in low-resource settings.  \nAbstract  \nGlobal 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  \nsettings globally.  \nIntroduction  \nSignificant progress has been made in advancing imaging technologies to enable extraction of high-quality information from light microscopy images, thus enabling in-depth characterisation of processes spanning areas of research including tissue biology, immunology, microbiology and oncology. Moreover, by adding repeated measurements of live cell cultures to generate time-lapse imaging data, it is also possible to gain insight into dynamic changes in cellular behaviour over time. However, such measurements typically rely on data obtained using high-cost systems, including advanced confocal, superresolution or ptychographic microscopy-a technique that computationally reconstructs highresolution images from overlapping illumination diffraction patterns. In addition, emerging machine learning-based image analysis tools and app","cbCaidnKYdqlcPfK","https://ap.wps.com/l/cbCaidnKYdqlcPfK","pdf",311442,35,"English","# Summary statement\n# Abstract\n# Introduction","[{\"question\":\"What workshop topic does the article focus on?\",\"answer\":\"The workshop focuses on advancing timelapse imaging and machine learning-based phenotyping, especially to address barriers in low-resource research environments.\"},{\"question\":\"Which major challenges hinder adoption of advanced bioimaging tools?\",\"answer\":\"High costs of imaging equipment and limited access to analysis software are identified as key hurdles.\"},{\"question\":\"What practical approach does the article emphasize for overcoming barriers?\",\"answer\":\"It emphasizes integrating combined imaging hardware with machine learning-based approaches, supported by actionable strategies discussed during the workshop.\"}]","Removing Barriers to Advanced Imaging and Machine Learning-based Analysis - Key Observations and Actionable Strategies | PDF",1789641985,88]