ArticleJournal of microscopy2024
The rise of data-driven microscopy powered by machine learning.
Article in Journal of microscopy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- DySTrack - a modular smart microscopy tool for live tracking of dynamic samples on modern commercial microscopes.Journal of cell science · 2026Article
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- O-SNAP uncovers nanoscale chromatin remodeling in dedifferentiation and stress responses.Nature communications · 2026Article
- Review
- JuNkIE-CLImax: exploring multidimensional images in notebooks and the terminal.microPublication biology · 2026Article
- Self-Driving Microscopes: AI Meets Super-Resolution Microscopy.Small methods · 2026Review
- Advanced Microscopy Techniques.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Visualizing the multidimensional landscape of biological variation in modern microscopy.Frontiers in bioinformatics · 2026Article
- Semi-automated image acquisition and analyses for broad users utilizing macro keyboards.Microscopy (Oxford, England) · 2025Article
- OoCount: a machine-learning-based approach to mouse ovarian follicle counting and classification†.Biology of reproduction · 2025Article
- Three-photon microscopy: an emerging technique for deep intravital brain imaging.Nature reviews. Neuroscience · 2025Review
- Ultrafast tomograms in a flash.Science advances · 2025Review
- Image analysis optimization for nanowire-based optical detection of molecules.Nanophotonics (Berlin, Germany) · 2025Article
- AI analysis of super-resolution microscopy: Biological discovery in the absence of ground truth.The Journal of cell biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
Optical 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 challenges and future outlook. Overall, intelligent microscopes that can sense, analyse and adapt promise to transform optical imaging by opening new experimental possibilities.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.