DOI: 10.5061/dryad.9ghx3ffsm
title: Reducing Hepatitis C diagnostic disparities with a point of care assay for HCV antigen detection

description: Viral hepatitis continues to be a significant global health issue, with
 chronic hepatitis B (HBV) and hepatitis C (HCV) responsible for
 approximately 1 million deaths each year, primarily due to complications
 such as liver cancer and cirrhosis. Annually, more than 1.5 million
 individuals contract HCV, with vulnerable populations, including American
 Indians and Alaska Natives (AI/AN), being disproportionately affected.
 Although direct-acting antivirals (DAAs) have proven highly effective, the
 timely and accurate diagnosis of HCV remains a major challenge,
 particularly in settings with limited resources. The current two-step HCV
 testing approach is both costly and time-intensive, often resulting in
 patient loss before appropriate care is administered. Point-of-care (POC)
 HCV antigen (Ag) testing presents a viable alternative, offering the
 potential for early detection, even during the acute phase of infection.
 However, there is currently no FDA-approved POC HCV Ag test that meets the
 required sensitivity and specificity for detecting low viral loads.
description: # Reducing Hepatitis C diagnostic disparities with a point of care assay
 for HCV antigen detection ## Design files All the designs for the 3d
 printed objects and laser cut objects used in the manuscript. The file
 "3D printing parts.f3d" contains all the 3D designs of the 3D
 printed parts used in the manuscript, and can be opened with Autocad
 Fusion 360. The file "Layer-1.ai" contains the 2D designs of
 laser cutting path of the 1st layer of PMMA sheet of the microfluidic
 chip  used in the manuscript, and can be opened with Adobe Illustrator. 
 The file "Layer-2.ai" contains the 2D designs of laser cutting
 path of the 2nd layer of PMMA sheet of the microfluidic chip  used in the
 manuscript, and can be opened with Adobe Illustrator.  The file
 "Layer-3.ai" contains the 2D designs of laser cutting path of
 the 3rd layer of PMMA sheet of the microfluidic chip  used in the
 manuscript, and can be opened with Adobe Illustrator.  The file
 "Layer-4.ai" contains the 2D designs of laser cutting path of
 the 4th layer of PMMA sheet of the microfluidic chip  used in the
 manuscript, and can be opened with Adobe Illustrator.  The file
 "Layer-5.ai" contains the 2D designs of laser cutting path of
 the 5th layer of PMMA sheet of the microfluidic chip  used in the
 manuscript, and can be opened with Adobe Illustrator.  #### Requirements
 Autocad Fusion 360 Adobe Illustrator  ### Microfluidic code Running codes
 for the microfluidic device. ##### Requirements Arduino IDE 2.3.4  ##
 SPyDERMAN [Smartphone-based Pathogen detection resource multiplier using
 adversarial networks
 (SPyDERMAN)](https://pubs.acs.org/doi/pdf/10.1021/acsnano.0c06807) This is
 the Pytorch implementation for our paper [Mobile Health (mHealth) Viral
 Diagnostics Enabled with Adaptive Adversarial
 Learning](https://pubs.acs.org/doi/pdf/10.1021/acsnano.0c06807).
 **Abstract:** &gt; 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 smartphone-based
 pathogen detection resource multiplier using adversarial networks
 (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. ## Requirements *
 Python 3.5 * Pytorch 1.4.0 * PyYAML 5.3.1 * scikit-image 0.14.0 *
 scikit-learn 0.20.0 * SciPy 1.1.0 * opencv-python 4.2.0.34 * Matplotlib
 3.0.0 * NumPy 1.15.2 * TensorFlow 1.10.0 (For TensorBoard) ### SPyDERMAN
 ## Usage ### Generating synthetic data library: Run `python
 scripts/stgan.py` ### Adversarial training with data library and target
 virus: Run ` python scripts/train_image_.py --dset "target viral
 dataset location"` example: ` python scripts/train_image_.py --dset
 "COVID19"` ## Citing We encourage citation of our paper if you
 use our code in your research: ``` @article{shokr2020mobile, title={Mobile
 Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial
 Learning}, author={Shokr, Ahmed and Pacheco, Luis GC and Thirumalaraju,
 Prudhvi and Kanakasabapathy, Manoj Kumar and Gandhi, Jahnavi and Kartik,
 Deeksha and Silva, Filipe SR and Erdogmus, Eda and Kandula, Hemanth and
 Luo, Shenglin and others}, journal={ACS nano}, year={2020}, publisher={ACS
 Publications} } ``` ## Contact If you have any questions, please contact
 us via hshafiee[at]bwh.harvard.edu.