ArticleLight, science & applications2018
Motility-based label-free detection of parasites in bodily fluids using holographic speckle analysis and deep learning.
Article in Light, science & applications, 2018. 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.
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Who cites it
25 citing papers in PubMed, 61 citations in OpenAlex.
- New Methodologies as Opportunities in the Study of Bacterial Biofilms, Including Food-Related Applications.Microorganisms · 2025Review
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- All-optical dual module platform for motility-based functional scrutiny of microencapsulated probiotic bacteria.Biomedical optics express · 2024Article
- Trajectory-driven computational analysis for element characterization in Trypanosoma cruzi video microscopy.PloS one · 2024Article
- Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning.Nature biomedical engineering · 2023Article
- Extracting particle size distribution from laser speckle with a physics-enhanced autocorrelation-based estimator (PEACE).Nature communications · 2023Article
- Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry.Nature photonics · 2022Article
- Automated wide-field malaria parasite infection detection using Fourier ptychography on stain-free thin-smears.Biomedical optics express · 2022Article
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Examination of blood samples using deep learning and mobile microscopy.BMC bioinformatics · 2022Article
- Deep learning for microscopic examination of protozoan parasites.Computational and structural biotechnology journal · 2022Review
- Dehydration of plant cells shoves nuclei rotation allowing for 3D phase-contrast tomography.Light, science & applications · 2021Article
- High-speed, three-dimensional imaging reveals chemotactic behaviour specific to human-infectiveeLife · 2021Article
- Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry.Nature communications · 2021Article
- Detection of circulating microfilariae in canine EDTA blood using lens-free technology: preliminary results.Journal of veterinary diagnostic investigation : official publication of the American Association of Veterinary Laboratory Diagnosticians, Inc · 2021Article
- Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021Article
- Lensless, reflection-based dark-field microscopy (RDFM) on a CMOS chip.Biomedical optics express · 2020Article
- Biospeckle-characterization of hairy root cultures using laser speckle photometry.Engineering in life sciences · 2020Article
- Compact off-axis holographic slide microscope: design guidelines.Biomedical optics express · 2020Article
- Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning.Light, science & applications · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors at 2 institutions in 1 country.
Funding
Abstract
Parasitic infections constitute a major global public health issue. Existing screening methods that are based on manual microscopic examination often struggle to provide sufficient volumetric throughput and sensitivity to facilitate early diagnosis. Here, we demonstrate a motility-based label-free computational imaging platform to rapidly detect motile parasites in optically dense bodily fluids by utilizing the locomotion of the parasites as a specific biomarker and endogenous contrast mechanism. Based on this principle, a cost-effective and mobile instrument, which rapidly screens ~3.2 mL of fluid sample in three dimensions, was built to automatically detect and count motile microorganisms using their holographic time-lapse speckle patterns. We demonstrate the capabilities of our platform by detecting trypanosomes, which are motile protozoan parasites, with various species that cause deadly diseases affecting millions of people worldwide. Using a holographic speckle analysis algorithm combined with deep learning-based classification, we demonstrate sensitive and label-free detection of trypanosomes within spiked whole blood and artificial cerebrospinal fluid (CSF) samples, achieving a limit of detection of ten trypanosomes per mL of whole blood (~five-fold better than the current state-of-the-art parasitological method) and three trypanosomes per mL of CSF. We further demonstrate that this platform can be applied to detect other motile parasites by imaging
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.