[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118180-en":3,"doc-seo-118180-105":30,"detail-sidebar-cat-0-en-105":84},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118180,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","The Rise of Data-Driven Microscopy - powered by Machine Learning","Optical microscopy is essential in life sciences, yet conventional approaches force trade-offs among speed, resolution, field-of-view, signal-to-noise, multiplexing, z-depth, and phototoxicity. Data-driven microscopes address these limits through real-time feedback loops that connect acquisition and computational image analysis. This review explains core data-driven concepts and relevant machine-learning methods, then surveys pioneering and recent workflow integrations, including illumination optimization, modality switching, acquisition-rate control, and trigger-based targeted experiments, while outlining remaining challenges and future outlook.","The Rise of Data-Driven Microscopy powered  \nby Machine Learning  \nLeonor Morgado1 , Estibaliz Gómez-de-Mariscal1 , Hannah S. Heil1,, and Ricardo Henriques1,2,  \n1 Instituto Gulbenkian de Ciência, Oeiras, Portugal  \n2 MRC-Laboratory for Molecular Cell Biology. University College London, London, United Kingdom  \nOptical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challengesand future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities.  \ndata-driven | reactive microscopy | image analysis | machine learning Correspondence: (H. S. Heil) [hsheil@igc.gulbenkian.pt](hsheil@igc.gulbenkian.pt),(R. Henriques) rjhen[riques@igc.gulbenkian.pt](riques@igc.gulbenkian.pt) [r.henriques@ucl.ac.uk](r.henriques@ucl.ac.uk)  \nIntroduction  \nOptical microscopy techniques, such as brightfield, phase contrast, fluorescence, and super-resolution imaging, are widely used in life sciences to obtain valuable spatiotemporal information for studying cells and model organisms. However, these techniques have certain limitations with respect to critical parameters such as resolution, acquisition speed, signal to noise ratio, field of view, extent of multiplexing, zdepth dimensions and phototoxicity. The trade-offs between these critical imaging parameters are often represented within a \"pyramid of frustration\" (Fig. 1A) . Although improving hardware can extend capabilities, optimal balancing depends on the imaging context. Especially, as scientific research delves into more complex questions, trying to understand the mechanisms ofcell and infection biology at a molecular level in physiological context, traditional static microscopes may not be sufficient to capture relevant dynamics or rare events. Innovative efforts focus on overcoming these restrictions through integrated automation. Data-driven microscopes employ real-time data analysis to dynamically control and adapt acquisition (Fig. 1B) . The core concept involves introducing automated feedback loops between image-data interpretation and microscope parameters tuning. Quantitative metrics extracted via computational analysis then dictate adaptive protocols tailored to phenomena of interest. The system reacts to predefined observational triggers by optimising imaging  \nData-driven microscope: The data-driven microscope integrates advanced computational techniques into its imaging capabilities. It uses machine learning algorithms and real time data analysis to automatically adjust the acquisition parameters. This way it is possible to optimise imaging conditions, enhance image quality and extract meaningful information without heavy reliance on manual intervention.  \nparameters-such as excitation, stage position, and objective lenses-to capture critical events efficiently (Fig. 1C) .  \nImage analysis algorithms are pivotal in data-driven methodologies with customised approaches serving a large variety of situations. These approaches can use machine learning techniques to perform tasks such as classification, segmentation, tracking, and reconstruction without the need for explicit programming. By integrating machine learning, intelligent mi","cbCaijdH9NRnnA7N","https://ap.wps.com/l/cbCaijdH9NRnnA7N","pdf",5346346,1,7,"English","en",105,"# Introduction\n## Limitations of conventional optical microscopy\n## Data-driven microscopy and feedback loops\n## Machine learning in microscopy image analysis\n## Review scope and structure","[{\"question\":\"What future direction and challenges does the review emphasize?\",\"answer\":\"The review highlights remaining challenges and an outlook toward intelligent microscopes that can sense, analyze, and adapt in real time. Such systems are expected to expand optical imaging capabilities and experimental possibilities.\"}]","The Rise of Data-Driven Microscopy - powered by Machine Learning | PDF",1785682047,18,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":79,"head_meta":81,"extra_data":83,"updated_unix":28},"the-rise-of-data-driven-microscopy-powered-by-machine-learning","",{"@graph":36,"@context":78},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-rise-of-data-driven-microscopy-powered-by-machine-learning/118180/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72],{"name":73,"@type":74,"acceptedAnswer":75},"What future direction and challenges does the review emphasize?","Question",{"text":76,"@type":77},"The review highlights remaining challenges and an outlook toward intelligent microscopes that can sense, analyze, and adapt in real time. 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