AI-Assisted Plasmonic Diagnostics Platform for Osteoarthritis and Rheumatoid Arthritis With Biomarker Quantification...

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Diagnostic Technologies

Background:

• Osteoarthritis (OA) and rheumatoid arthritis (RA) are leading causes of disability and pain, increasing healthcare burdens.
• Current diagnostic methods for OA and RA can be complex, highlighting the need for improved diagnostic tools.
• Accurate and early diagnosis is crucial for effective management of these debilitating conditions.

Purpose of the Study:

• To develop and validate an innovative plasmonic diagnostics platform for rapid, label-free diagnosis of OA and RA.
• To enhance diagnostic accuracy and cost-effectiveness for arthritis detection.
• To explore the platform's potential for biomarker quantification and disease severity assessment.

Main Methods:

• Utilized a highly dense urchin-like gold nanoarchitecture (UGN) for enhanced surface plasmon resonance and Raman signal amplification.
• Analyzed synovial fluid (SVF) from OA and RA patients using the UGN-based sensing platform.
• Employed machine learning models for classification of Raman signals and hematology test results.

Main Results:

• Successfully classified OA and RA patient groups with high clinical sensitivity and specificity using Raman spectroscopy and machine learning.
• Identified metabolic biomarkers through advanced mathematical modeling (PCC and NMF) for improved arthritis quantification.
• Achieved successful discrimination of rheumatoid arthritis stages by analyzing hematology test results via the sensing platform.

Conclusions:

• The developed plasmonic diagnostics platform provides a versatile, affordable, and scalable solution for in-clinic arthritis diagnosis.
• This technology demonstrates significant potential for accurate, label-free detection and monitoring of OA and RA.
• The platform's adaptability suggests broader applications in biofluid analysis for diagnosing and managing various diseases.