ArticleLight, science & applications2022
Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network.
Article in Light, science & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 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
27 citing papers in PubMed.
- Artificial Intelligence in Foodborne Pathogen Detection from Sensing to Food Safety Systems: A Systematic Review.Foods (Basel, Switzerland) · 2026Review
- Label-free volumetric refractive-index imaging of fibrillar collagen architecture, assembly, and cell-associated remodeling.bioRxiv : the preprint server for biology · 2026Article
- One-day rapid sterility test for human-derived biopharmaceuticals.Nature biomedical engineering · 2026Article
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- The Fires of Isengard Have Spread:Pathogens (Basel, Switzerland) · 2026Article
- A comprehensive review of AI innovations for tackling antimicrobial resistance.Le infezioni in medicina · 2026Review
- Automated microbial recognition from gram-stained micrographs via CNN and metadata decision support.Frontiers in cellular and infection microbiology · 2026Article
- Article
- Lightweight and precise cell classification based on holographic tomography-derived refractive index point cloud.Journal of biomedical optics · 2025Article
- Spatial microfluidic holographic integrated platform for label-free and high-dimensional analysis of cancer heterogeneity.Nature communications · 2025Article
- How AI can help us beat AMR.npj antimicrobials and resistance · 2025Review
- Bidirectional in-silico clearing approach for deep refractive-index tomography using a sparsely sampled transmission matrix.Biomedical optics express · 2024Article
- Single-shot quantitative phase-fluorescence imaging using cross-grating wavefront microscopy.Scientific reports · 2024Article
- Simple Imaging System for Label-Free Identification of Bacterial Pathogens in Resource-Limited Settings.International journal of biomedical imaging · 2024Article
- On the use of deep learning for phase recovery.Light, science & applications · 2024Review
- Building an optics and photonics research ecosystem in South Korea: Collaborative innovation between academia and industry.Light, science & applications · 2023Article
- Enhanced functionalities of immune cells separated by a microfluidic lattice: assessment based on holotomography.Biomedical optics express · 2023Article
- Three-dimensional label-free morphology of CD8 + T cells as a sepsis biomarker.Light, science & applications · 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
Identifiers
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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.