ArticleACS nano2021
Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.
Article in ACS nano, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed, 41 citations in OpenAlex.
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Recent Advances in Microfluidic Chip Technology for Laboratory Medicine: Innovations and Artificial Intelligence Integration.Biosensors · 2026Review
- A foundational architecture for AI agents in healthcare.Cell reports. Medicine · 2025Review
- Review
- AI-Enabled Microfluidics for Respiratory Pathogen Detection.Sensors (Basel, Switzerland) · 2025Review
- Automated, Deep Learning-Enabled Immunoassay Microfluidic Cartridge for Viral Pathogen Detection.Advanced materials technologies · 2025Article
- Digitally Enabled AI-Interpreted Salivary Ferning-Based Ovulation Prediction: Feasibility Study.Journal of medical Internet research · 2025Article
- Machine learning in point-of-care testing: innovations, challenges, and opportunities.Nature communications · 2025Review
- Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV antigen detection.Science advances · 2025Article
- Next-generation CRISPR/Cas-based ultrasensitive diagnostic tools: current progress and prospects.RSC advances · 2024Review
- Advancing Microfluidic Immunity Testing Systems: New Trends for Microbial Pathogen Detection.Molecules (Basel, Switzerland) · 2024Review
- Generative artificial intelligence to produce high-fidelity blastocyst-stage embryo images.Human reproduction (Oxford, England) · 2024Article
- Smartphone-based point-of-care testing of the SARS-CoV-2: A systematic review.Scientific African · 2023Article
- Rapidly adaptable automated interpretation of point-of-care COVID-19 diagnostics.Communications medicine · 2023Article
- Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.Nature communications · 2023Review
- Application of microfluidic technologies on COVID-19 diagnosis and drug discovery.Acta pharmaceutica Sinica. B · 2023Review
- Review
- Merging microfluidics with luminescence immunoassays for urgent point-of-care diagnostics of COVID-19.Trends in analytical chemistry : TRAC · 2022Review
- Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip.Lab on a chip · 2022Article
- How Has COVID-19 Changed the Way We Do Virtual Care? A Scoping Review Protocol.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors at 3 institutions in 2 countries.
Funding
Abstract
Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed
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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.