ArticleBiosensors & bioelectronics2021
Human sensor-inspired supervised machine learning of smartphone-based paper microfluidic analysis for bacterial species classification.
Article in Biosensors & bioelectronics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 54 citations in OpenAlex.
- Bibliometric analysis of the application of artificial intelligence techniques in bacteriology: a PRISMA-guided research agenda.Frontiers in microbiology · 2025Pooled it
- Emerging Trends in Artificial Intelligence-Integrated Biochip Technologies for Biomedical Applications.Micromachines · 2026Review
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Rapid antithrombin assay from human blood plasma utilizing smartphone-based flow observation on paper chips.PLOS digital health · 2026Article
- Emerging Trends in Artificial Intelligence-Assisted Colorimetric Biosensors for Pathogen Diagnostics.Sensors (Basel, Switzerland) · 2026Review
- Deep learning-driven morphological fingerprinting: rapid, accurate and low-cost pathogen identification via the analysis of dried patterns of droplets.Journal of nanobiotechnology · 2025Article
- Deep learning enabled rapid detection of live bacteria in the presence of food debris.NPJ science of food · 2025Article
- Smartphone-based biosensing: a review of optical imaging, microfluidic integration, and AI-enhanced analysis.Mikrochimica acta · 2025Review
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- The Role of Artificial Intelligence in Advancing Biosensor Technology: Past, Present, and Future Perspectives.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Artificial intelligence-powered electrochemical sensor: Recent advances, challenges, and prospects.Heliyon · 2024Review
- Soil microbiome characterization and its future directions with biosensing.Journal of biological engineering · 2024Article
- eXtreme gradient boosting-based classification of bacterial mixtures in water and milk using wireless microscopic imaging of quorum sensing peptide-conjugated particles.Biosensors & bioelectronics · 2023Article
- Waterborne pathogens detection technologies: advances, challenges, and future perspectives.Frontiers in microbiology · 2023Review
- Paper-based sensors for bacteria detection.Nature reviews bioengineering · 2023Review
- Distinction of Different Colony Types by a Smart-Data-Driven Tool.Bioengineering (Basel, Switzerland) · 2022Article
- Machine learning classification of bacterial species using mix-and-match reagents on paper microfluidic chips and smartphone-based capillary flow analysis.Analytical and bioanalytical chemistry · 2022Article
- Overview of Rapid Detection Methods forFoods (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
Bacteria identification has predominantly been conducted using specific bioreceptors such as antibodies or nucleic acid sequences. This approach may be inappropriate for environmental monitoring when the user does not know the target bacterial species and for screening complex water samples with many unknown bacterial species. In this work, we investigate the supervised machine learning of the bacteria-particle aggregation pattern induced by the peptide sets identified from the biofilm-bacteria interface. Each peptide is covalently conjugated to polystyrene particles and loaded together with bacterial suspensions onto paper microfluidic chips. Each peptide interacts with bacterial species to a different extent, leading to varying sizes of particle aggregation. This aggregation changes the surface tension and viscosity of the liquid flowing through the paper pores, altering the flow velocity at different extents. A smartphone camera captures this flow velocity without being affected by ambient and environmental conditions, towards a low-cost, rapid, and field-ready assay. A collection of such flow velocity data generates a unique fingerprinting profile for each bacterial species. Support vector machine is utilized to classify the species. At optimized conditions, the training model can predict the species at 93.3% accuracy out of five bacteria: Escherichia coli, Staphylococcus aureus, Salmonella Typhimurium, Enterococcus faecium, and Pseudomonas aeruginosa. Flow rates are monitored for less than 6 s and the sample-to-answer assay time is less than 10 min. The demonstrated method can open a new way of analyzing complex biological and environmental samples in a biomimetic manner with machine learning classification.
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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.