Diagnosing heart failure (HF), which affects millions globally, requires reliable biomarker analysis. Current BNP (B-type natriuretic peptide) testing methods are time-consuming, labour-intensive, and depend on expensive, complex lab equipment. There’s a critical need for faster, more accessible point-of-care (POC) testing with high sensitivity and accuracy.
Core Features
- Innovative Sensors: The invention uses carbon nanotube thin-film (CNT-TF) sensors for detecting BNP levels. These sensors are functionalized to bind specifically to the BNP biomarker.
- Temporal Data Analysis: Employs electrochemical impedance spectroscopy (EIS) to measure BNP concentrations over time, generating detailed temporal impedance spectra.
- Machine Learning Integration: A convolutional neural network (CNN)-based machine learning model analyses the spectra to determine BNP levels, overcoming noise and variability issues in sensor performance.
Benefits
- Rapid and Accurate Diagnosis: Enables quick, reliable HF diagnosis without the need for complex lab setups.
- Ease of Use: Compact, handheld device suitable for bedside or POC testing.
- Cost Efficiency: Utilizes low-cost materials and avoids the need for highly trained professionals or bulky equipment.
- Scalability: The machine learning model adapts and self-learns, improving accuracy as more data becomes available.
Impact
This innovation can revolutionize HF diagnosis by making it more accessible and efficient, especially in resource-limited settings. It reduces hospital readmissions and aids in better clinical management of HF, potentially saving lives and lowering healthcare costs. Furthermore, its adaptability could expand applications to other biomarkers, broadening its societal and healthcare impact.