ArticleScience advances2025
Deep learning-enabled filter-free fluorescence microscope.
Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Emerging trends of fluorescence lifetime imaging microscopy (FLIM): advances, challenges, and prospects.Biophysics reports · 2026Article
- Technological innovations and high-throughput applications of light-sheet microscopy.Biophysics reports · 2026Article
- A Novel Hand-Held Spinning Platform with Centrifugal Microfluidics for Rapid, Cost-Effective Urinary Total Protein Detection at the Point of Care.Analytical chemistry · 2025Article
- Seeing through arthropod eyes: An AI-assisted, biomimetic approach for high-resolution, multi-task imaging.Science advances · 2025Article
- Advances in nucleic acid probe-based detection of gene point mutations: a review.Frontiers in chemistry · 2025Review
- AI-based hardware and software tools in microscopy to boost research in immunology and virology.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization of optical filtering sets increases the complexity, size, and cost of microscopic systems, making them less suitable for multifluorescence channel, high-speed imaging. Here, we present filter-free fluorescence microscopic imaging enabled with deep learning-based digital spectral filtering. This approach allows for automatic fluorescence channel selection after image acquisition and accurate prediction of fluorescence by computing color changes due to spectral shifts with the presence of excitation scattering. Fluorescence prediction for cells and tissues labeled with various fluorophores was demonstrated under different magnification powers. The technique offers accurate identification of labeling with robust sensitivity and specificity, achieving consistent results with the reference standard. Beyond fluorescence microscopy, the deep learning-enabled spectral filtering strategy has the potential to drive the development of other biomedical applications, including cytometry and endoscopy.
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Registered trials
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