ArticleNPJ digital medicine2020
Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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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
35 citing papers in PubMed.
- Article
- Deep learning-enhanced dual-mode multiplexed optical sensor for point-of-care diagnostics of cardiovascular diseases.Light, science & applications · 2026Article
- ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction.Genetics, selection, evolution : GSE · 2026Article
- Personalized Medication for Chronic Diseases Using Multimodal Data-Driven Chain-of-Decisions.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Leveraging Deep Learning to Address Diagnostic Challenges with Insufficient Image Data.ACS sensors · 2025Article
- Advancements and Applications of Lateral Flow Assays (LFAs): A Comprehensive Review.Sensors (Basel, Switzerland) · 2025Review
- Paper-Based Vertical Flow Assays for in Vitro Diagnostics and Environmental Monitoring.ACS sensors · 2025Review
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Deep Learning-Enhanced Chemiluminescence Vertical Flow Assay for High-Sensitivity Cardiac Troponin I Testing.Small (Weinheim an der Bergstrasse, Germany) · 2025Article
- Patient-centred newer testing methods for major cardiac disorders.Indian journal of thoracic and cardiovascular surgery · 2025Review
- Deep Learning-Enhanced Paper-Based Vertical Flow Assay for High-Sensitivity Troponin Detection Using Nanoparticle Amplification.ACS nano · 2024Article
- Rapid single-tier serodiagnosis of Lyme disease.Nature communications · 2024Article
- AI-Based Metamaterial Design.ACS applied materials & interfaces · 2024Review
- Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2024Review
- Comparison and Verification of Three Algorithms for Accuracy Improvement of Quartz Resonant Pressure Sensors.Micromachines · 2023Article
- Innovative solutions for disease management.Bioelectronic medicine · 2023Article
- Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.Communications medicine · 2023Article
- Engineering Innovative Interfaces for Point-of-Care Diagnostics.Current opinion in colloid & interface science · 2023Review
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Lateral Flow Microimmunoassay (LFµIA) for the Reliable Quantification of Allergen Traces in Food Consumables.Biosensors · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a deep learning-based framework to design and quantify point-of-care sensors. As a use-case, we demonstrated a low-cost and rapid paper-based vertical flow assay (VFA) for high sensitivity C-Reactive Protein (hsCRP) testing, commonly used for assessing risk of cardio-vascular disease (CVD). A machine learning-based framework was developed to (1) determine an optimal configuration of immunoreaction spots and conditions, spatially-multiplexed on a sensing membrane, and (2) to accurately infer target analyte concentration. Using a custom-designed handheld VFA reader, a clinical study with 85 human samples showed a competitive coefficient-of-variation of 11.2% and linearity of
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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.