Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning
Open accessAbstract
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 s martphone-based p athogen de tection r esource m ultiplier using a dversarial n etworks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples ( n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.
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The deployment of lightweight AI models on edge devices revolutionizes medical AI by enabling real-time, localized data processing with minimal latency.
“The deployment of lightweight AI models on edge devices revolutionizes medical AI by enabling real-time, localized data processing with minimal latency.132,133”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [133]
The Abstract (page not supplied) describes mobile-device image processing as promising for smartphone-based infectious-disease diagnostics, and Results and Discussion calls for accurate on-phone image processing. The paper does not establish that lightweight AI models were deployed on edge devices, that processing had minimal latency, or that this approach revolutionizes medical AI generally. (LLM, conf=0.84)
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Cites
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380 · see citing works
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2020) · Communications of the ACM · doi:10.1145/3422622 · Cited by 18,384 · see citing works
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Domain-Adversarial Training of Neural Networks
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2017) · Advances in Computer Vision and Pattern Recognition · doi:10.1007/978-3-319-58347-1_10 · Cited by 8,318 · see citing works
Virological assessment of hospitalized patients with COVID-2019
Wölfel, R., Corman, V. M., Guggemos, W., Seilmaier, M., Zange, S., Müller, M. A., Niemeyer, D., Jones, T. C., Vollmar, P., Rothe, C., et al. (2020) · Nature · doi:10.1038/s41586-020-2196-x · Cited by 6,661 · cited 1× · see citing works
“17,18 The signal was also reliably differentiated from results obtained with influenza A and B samples (Figure 2e, right panel).”
ResultAnalyzing and Improving the Image Quality of StyleGAN
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00813 · Cited by 6,332 · see citing works
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209 · see citing works
Conditional Adversarial Domain AdaptationPreprint
Long, M., Cao, Z., Wang, J., & Jordan, M. I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.10667 · Cited by 1,963 · see citing works
Secure, privacy-preserving and federated machine learning in medical imaging
Kaissis, G. A., Makowski, M. R., Rückert, D., & Braren, R. F. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0186-1 · Cited by 1,501 · see citing works
Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components
Pardee, K., Green, A. A., Takahashi, M. K., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.059 · Cited by 1,444 · see citing works
Laboratory Diagnosis of COVID-19: Current Issues and Challenges
Tang, Y., Schmitz, J. E., Persing, D. H., & Stratton, C. W. (2020) · Journal of Clinical Microbiology · doi:10.1128/jcm.00512-20 · Cited by 1,234 · see citing works
CRISPR/Cas Systems towards Next-Generation Biosensing
Li, Y., Li, S., Wang, J., & Liu, G. (2019) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.12.005 · Cited by 915 · see citing works
Loynachan, C. N., Thomas, M. R., Gray, E. R., Richards, D. A., Kim, J., Miller, B. S., Brookes, J. C., Agarwal, S., Chudasama, V., McKendry, R. A., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b06229 · Cited by 408 · see citing works
COVID-19 diagnostics in context
Weissleder, R., Lee, H., Ko, J., & Pittet, M. J. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.abc1931 · Cited by 397 · see citing works
The role of quantitative hepatitis B surface antigen revisited
Cornberg, M., Wong, V. W., LOCARNINI, S. A., et al. (2016) · Journal of Hepatology · doi:10.1016/j.jhep.2016.08.009 · Cited by 373 · see citing works
Point-of-care testing based on smartphone: The current state-of-the-art (2017–2018)
Liu, J., Geng, Z., Fan, Z., Liu, J., & Chen, H. (2019) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2019.01.068 · Cited by 334 · see citing works
Taking connected mobile-health diagnostics of infectious diseases to the field
Wood, C. S., Thomas, M. R., Budd, J., Mashamba-Thompson, T. P., Herbst, K., Pillay, D., Peeling, R. W. W., Johnson, A. M., McKendry, R. A., & Stevens, M. M. (2019) · Nature · doi:10.1038/s41586-019-0956-2 · Cited by 325 · see citing works
A Survey on the New Generation of Deep Learning in Image Processing
Jiao, L., & Zhao, J. K. (2019) · IEEE Access · doi:10.1109/access.2019.2956508 · Cited by 307 · see citing works
Deep Learning with Microfluidics for Biotechnology
Riordon, J., Sovilj, D., Sanner, S., Sinton, D. A., & Young, E. W. K. (2018) · Trends in biotechnology · doi:10.1016/j.tibtech.2018.08.005 · Cited by 225 · see citing works
Smartphone‐based clinical diagnostics: towards democratization of evidence‐based health care
Hernández-Neuta, I., Neumann, F., Brightmeyer, J., Tis, T. B., Madaboosi, N., Wei, Q., Özcan, A., & Nilsson, M. F. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12820 · Cited by 207 · see citing works
Point-of-Care RNA-Based Diagnostic Device for COVID-19
Yang, T., Wang, Y.-C., Shen, C.-F., & Cheng, C.-M. (2020) · Diagnostics · doi:10.3390/diagnostics10030165 · Cited by 137 · see citing works
Interfacing Pathogen Detection with Smartphones for Point-of-Care Applications
Ding, X., Mauk, M. G., Yin, K., Kadimisetty, K., & Liu, C. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.8b04973 · Cited by 131 · see citing works
Acute On‐Chip HIV Detection Through Label‐Free Electrical Sensing of Viral Nano‐Lysate
Shafiee, H., Jahangir, M., İnci, F., et al. (2013) · Small · doi:10.1002/smll.201202195 · Cited by 100 · see citing works
Self-citationDNA engineered micromotors powered by metal nanoparticles for motion based cellphone diagnostics
Draz, M. S., Kochehbyoki, K. M., Vasan, A., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06727-8 · Cited by 98 · see citing works
Approaches for simplified HCV diagnostic algorithms
Fourati, S., Feld, J. J., Chevaliez, S., et al. (2018) · Journal of the International AIDS Society · doi:10.1002/jia2.25058 · Cited by 64 · see citing works
Nanoparticle-enhanced electrical detection of Zika virus on paper microchips
Draz, M. S., Venkataramani, M., Lakshminarayanan, H., et al. (2018) · Nanoscale · doi:10.1039/c8nr01646a · Cited by 54 · see citing works
Chen, H., Li, Z. Y., Zhang, L., et al. (2019) · Angewandte Chemie International Edition · doi:10.1002/anie.201906856 · Cited by 49 · see citing works
Farshidfar, N., & Hamedani, S. (2020) · Molecular Diagnosis & Therapy · doi:10.1007/s40291-020-00477-4 · Cited by 36 · see citing works
TarGAN: Generating target data with class labels for unsupervised domain adaptation
Lv, F., Zhu, J., Yang, G., & Duan, L. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.02.015 · Cited by 27 · see citing works
Rapid functionalisation and detection of viruses via a novel Ca2+-mediated virus-DNA interaction
Robb, N. C., Taylor, J. M., Kent, A., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-52759-5 · Cited by 19 · see citing works
Semi-supervised image classification via attention mechanism and generative adversarial network
Xiang, X., Yu, Z., Lv, N., et al. (2020) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2557747 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [28]Mirza M; Osindero S Conditional Generative Adversarial Nets. arXiv:1411.178v1 2014.
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A foundational architecture for AI agents in healthcare
Liu, F., Niu, Y., Zhang, Q., Wang, K., Dong, Z., Wong, I. N., Cheng, L., Li, T., Duan, L., Li, K., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102374 · Cited by 41 · see citing works
- Authors
- Shokr, A., Pacheco, L. G. C., Thirumalaraju, P., Kanakasabapathy, M. K., Gandhi, J., Kartik, D., Silva, F. S. R., Erdogmus, E., Kandula, H., Luo, S., Yu, X. G., Chung, R. T., Li, J. Z., Kuritzkes, D. R., & Shafiee, H.
- Affiliations
- Brigham and Women's Hospital (US)
- Universidade Federal da Bahia (BR)
- Harvard University (US)
- Corresponding author
- Shafiee, Hadi
- ORCID
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-11-23
- Volume
- 15
- Issue
- 1
- Pages
- 665-673
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- PMC
- PMC8299938
- OpenAlex
- W3108939174
- Cited by
- 45
- References
- 31
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 7 · 2023: 10 · 2022: 7 · 2021: 5
- FWCI
- 1.73
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- ACS Nano (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Funda??o de Amparo ? Pesquisa do Estado da Bahia
- National Institute of Allergy and Infectious Diseases (R01AI118502, R01AI138800, R61AI140489)
- Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico
- Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Biosensors and Analytical Detection
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- smartphone-based diagnostics, virus detection, synthetic image generation, adversarial neural networks, CRISPR-based diagnostics, SARS-CoV-2 antigen detection, emerging viruses, epidemic preparedness
- MeSH terms
- Point-of-Care Testing, Smartphone, Deep Learning, COVID-19, COVID-19 Testing, Antigens, Viral, Communicable Disease Control, Disaster Planning, Humans, Image Processing, Computer-Assisted, Platinum, Public Health, Signal Processing, Computer-Assisted, Reproducibility of Results, Neural Networks, Computer, Telemedicine, Metal Nanoparticles, CRISPR-Cas Systems
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