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Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV
Hui Chen1, Yuxin Gao1, Gaojian Li1
1Division of Engineering in Medicine, Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02139, USA.
Insights
A new smartphone-based test offers accurate, low-cost diagnosis for Hepatitis C virus (HCV) infection. This point-of-care (POC) assay aims to improve early detection and reduce health disparities for vulnerable populations.
Area of Science:
- Hepatology and Virology
- Biomedical Engineering
- Point-of-Care Diagnostics
Background:
- Viral hepatitis, particularly chronic Hepatitis B (HBV) and Hepatitis C (HCV), causes significant global mortality, mainly from liver cancer and cirrhosis.
- HCV affects over 1.5 million people annually, with disproportionate impacts on vulnerable groups like American Indians and Alaska Natives (AI/AN).
- Current multi-step HCV diagnostic methods are costly and time-consuming, hindering timely treatment and contributing to patient attrition.
Purpose of the Study:
- To develop a novel, automated, point-of-care (POC) assay for Hepatitis C virus (HCV) antigen detection.
- To create a cost-effective and portable diagnostic solution to overcome limitations of current HCV testing strategies.
- To improve accessibility and equity in HCV diagnosis, especially for underserved populations such as AI/AN.
Main Methods:
- Development of a smartphone-based POC HCV antigen (Ag) assay integrating microfluidics and platinum nanoparticles.
- Utilization of deep learning algorithms for automated image processing and analysis of assay results.
- Validation of the assay's performance characteristics, including sensitivity and specificity.
Main Results:
- The developed smartphone-based POC HCV Ag assay demonstrated an overall accuracy of 94.59%.
- The assay is fully automated, portable, and cost-effective, addressing key challenges in current HCV diagnostics.
- The technology shows potential for rapid, reliable HCV detection in resource-limited settings.
Conclusions:
- The novel smartphone-based POC HCV Ag assay offers a promising solution for accurate and accessible HCV diagnosis.
- This technology has the potential to significantly reduce HCV-related health disparities, particularly among AI/AN communities.
- Improved diagnostic accessibility can lead to earlier treatment initiation and better patient outcomes for HCV infection.
Abstract:
Viral hepatitis remains a major global health issue, with chronic hepatitis B (HBV) and hepatitis C (HCV) causing approximately 1 million deaths annually, primarily due to liver cancer and cirrhosis. More than 1.5 million people contract HCV each year, disproportionately affecting vulnerable populations, including American Indians and Alaska Natives (AI/AN). While direct-acting antivirals (DAAs) are highly effective, timely and accurate HCV diagnosis remains a challenge, particularly in resource-limited settings. The current two-step HCV testing process is costly and time-intensive, often leading to patient loss before treatment. Point-of-care (POC) HCV antigen (Ag) testing offers a promising alternative, but no FDA-approved test meets the required sensitivity and specificity. To address this, we developed a fully automated, smartphone-based POC HCV Ag assay using platinum nanoparticles, deep learning image processing, and microfluidics. With an overall accuracy of 94.59%, this cost-effective, portable device has the potential to reduce HCV-related health disparities, particularly among AI/AN populations, improving accessibility and equity in care.
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