ArticleScientific reports2023
Machine learning at the edge for AI-enabled multiplexed pathogen detection.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 23 citations in OpenAlex.
- Biochip-simulated genotype signals enable accurate and interpretable AMR prediction via machine learning.Frontiers in medicine · 2026Article
- Bridging oral and systemic health: exploring pathogenesis, biomarkers, and diagnostic innovations in periodontal disease.Infection · 2025Review
- Integration of Artificial Intelligence in Biosensors for Enhanced Detection of Foodborne Pathogens.Biosensors · 2025Review
- "CLADE-FINDER":Microorganisms · 2024Article
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Application of machine learning based genome sequence analysis in pathogen identification.Frontiers in microbiology · 2024Review
- The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review.Sensors (Basel, Switzerland) · 2023Review
- Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies.Antimicrobial agents and chemotherapy · 2023Article
- Adaptive time modulation technique for multiplexed on-chip particle detection across scales.Optica · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Multiplexed detection of biomarkers in real-time is crucial for sensitive and accurate diagnosis at the point of use. This scenario poses tremendous challenges for detection and identification of signals of varying shape and quality at the edge of the signal-to-noise limit. Here, we demonstrate a robust target identification scheme that utilizes a Deep Neural Network (DNN) for multiplex detection of single particles and molecular biomarkers. The model combines fast wavelet particle detection with Short-Time Fourier Transform analysis, followed by DNN identification on an AI-specific edge device (Google Coral Dev board). The approach is validated using multi-spot optical excitation of Klebsiella Pneumoniae bacterial nucleic acids flowing through an optofluidic waveguide chip that produces fluorescence signals of varying amplitude, duration, and quality. Amplification-free 3× multiplexing in real-time is demonstrated with excellent specificity, sensitivity, and a classification accuracy of 99.8%. These results show that a minimalistic DNN design optimized for mobile devices provides a robust framework for accurate pathogen detection using compact, low-cost diagnostic devices.
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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.