Reducing hepatitis C diagnostic disparities with a fully automated deep learning-enabled microfluidic system for HCV...

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.