Real-time kidney function assessment using advanced bioimpedance and optical sensing
Chronic Kidney Disease (CKD) affects 850+ million people globally. Early detection is critical for preventing progression to kidney failure, yet most cases go undiagnosed until late stages due to:
The first non-invasive, real-time kidney function monitoring system that uses advanced sensing and AI to predict eGFR within seconds:
Multi-modal biosensors collect real-time physiological signals
WebSocket-based data streaming & preprocessing
ML model extracts features & predicts eGFR
Real-time dashboard with medical recommendations
Advanced bioimpedance spectroscopy across 5 frequency points for comprehensive tissue characterization
Photoplethysmography for non-invasive cardiovascular monitoring integrated with kidney assessment
Sub-second WebSocket communication enables live data visualization and immediate clinical feedback
Algorithm validated against laboratory eGFR measurements with medical-grade accuracy benchmarks
Longitudinal data tracking enables early detection of kidney function deterioration
Explainable ML models show feature importance for clinical decision support
Diagnostic Accuracy
Analysis Time
Monitoring
Non-invasive
Developed as a complete healthcare AI system showcasing real-world application of biomedical engineering, embedded systems, and machine learning. The project demonstrates seamless integration from hardware sensor data through ML processing to clinical-grade visualization.