ArticleLight, science & applications2021
Label-free SARS-CoV-2 detection and classification using phase imaging with computational specificity.
Article in Light, science & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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
25 citing papers in PubMed, 64 citations in OpenAlex.
- Article
- Enhanced detection of malaria infected red blood cells through phase driven classification.Scientific reports · 2025Article
- BlurryScope enables compact, cost-effective scanning microscopy for HER2 scoring using deep learning on blurry images.NPJ digital medicine · 2025Article
- Advances in Wastewater-Based Epidemiology for Pandemic Surveillance: Methodological Frameworks and Future Perspectives.Microorganisms · 2025Review
- Cell-TIMP: Cellular Trajectory Inference Based on Morphological Parameters.Nano letters · 2025Article
- Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling.Nature communications · 2024Article
- Quantitative phase imaging techniques for measuring scattering properties of cells and tissues: a review-part I.Journal of biomedical optics · 2024Review
- Off-axis digital lensless holographic microscopy based on spatially multiplexed interferometry.Journal of biomedical optics · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Roadmap on Label-Free Super-Resolution Imaging.Laser & photonics reviews · 2023Article
- Platelet aggregates detected using quantitative phase imaging associate with COVID-19 severity.Communications medicine · 2023Article
- Artificial intelligence-enabled quantitative phase imaging methods for life sciences.Nature methods · 2023Review
- Quantitative phase imaging for characterization of single cell growth dynamics.Lasers in medical science · 2023Review
- Phenotyping neuroblastoma cells through intelligent scrutiny of stain-free biomarkers in holographic flow cytometry.APL bioengineering · 2023Article
- Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.Nature biomedical engineering · 2023Article
- Diffractive interconnects: all-optical permutation operation using diffractive networks.Nanophotonics (Berlin, Germany) · 2023Article
- Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization.Scientific reports · 2023Article
- Digital staining facilitates biomedical microscopy.Frontiers in bioinformatics · 2023Article
- White blood cell detection, classification and analysis using phase imaging with computational specificity (PICS).Scientific reports · 2022Article
- GANscan: continuous scanning microscopy using deep learning deblurring.Light, science & applications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
Abstract
Efforts to mitigate the COVID-19 crisis revealed that fast, accurate, and scalable testing is crucial for curbing the current impact and that of future pandemics. We propose an optical method for directly imaging unlabeled viral particles and using deep learning for detection and classification. An ultrasensitive interferometric method was used to image four virus types with nanoscale optical path-length sensitivity. Pairing these data with fluorescence images for ground truth, we trained semantic segmentation models based on U-Net, a particular type of convolutional neural network. The trained network was applied to classify the viruses from the interferometric images only, containing simultaneously SARS-CoV-2, H1N1 (influenza-A virus), HAdV (adenovirus), and ZIKV (Zika virus). Remarkably, due to the nanoscale sensitivity in the input data, the neural network was able to identify SARS-CoV-2 vs. the other viruses with 96% accuracy. The inference time for each image is 60 ms, on a common graphic-processing unit. This approach of directly imaging unlabeled viral particles may provide an extremely fast test, of less than a minute per patient. As the imaging instrument operates on regular glass slides, we envision this method as potentially testing on patient breath condensates. The necessary high throughput can be achieved by translating concepts from digital pathology, where a microscope can scan hundreds of slides automatically.
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.