ArticleView (Beijing, China)2024
Frontiers in artificial intelligence-directed light-sheet microscopy for uncovering biological phenomena and multi-organ imaging.
Article in View (Beijing, China), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- From Static to Dynamic: Fluorescence Imaging Technology Advances Precise Embryo Evaluation.Small methods · 2026Review
- Chemically Programmed Prodrug Nanoparticles for Precise Oral Dopamine Delivery to Deep Brain Regions in Parkinson's Disease.Advanced materials (Deerfield Beach, Fla.) · 2026Article
- Refractive Index-Corrected Light-Sheet Microscopy for Macro-View Cardiovascular Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advances in tissue optical clearing for 3D imaging in large animal.Frontiers of optoelectronics · 2025Review
- Machine learning-assisted point-of-care diagnostics for cardiovascular healthcare.Bioengineering & translational medicine · 2025Review
- Dual-mode-driven nanomotors targeting inflammatory macrophages for the MRI and synergistic treatment of atherosclerosis.Journal of nanobiotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Light-sheet fluorescence microscopy (LSFM) introduces fast scanning of biological phenomena with deep photon penetration and minimal phototoxicity. This advancement represents a significant shift in 3-D imaging of large-scale biological tissues and 4-D (space + time) imaging of small live animals. The large data associated with LSFM requires efficient imaging acquisition and analysis with the use of artificial intelligence (AI)/machine learning (ML) algorithms. To this end, AI/ML-directed LSFM is an emerging area for multi-organ imaging and tumor diagnostics. This review will present the development of LSFM and highlight various LSFM configurations and designs for multi-scale imaging. Optical clearance techniques will be compared for effective reduction in light scattering and optimal deep-tissue imaging. This review will further depict a diverse range of research and translational applications, from small live organisms to multi-organ imaging to tumor diagnosis. In addition, this review will address AI/ML-directed imaging reconstruction, including the application of convolutional neural networks (CNNs) and generative adversarial networks (GANs). In summary, the advancements of LSFM have enabled effective and efficient post-imaging reconstruction and data analyses, underscoring LSFM's contribution to advancing fundamental and translational research.
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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.