AI-Enhanced High-Density NIRS Patch for Real-Time Brain Layer Oxygenation Monitoring in Neurological Emergencies

TL;DR

This paper presents an AI-enhanced high-density NIRS system capable of real-time, layer-specific brain oxygenation monitoring, significantly improving accuracy and clinical utility in neurological emergencies.

Contribution

It introduces a novel AI-driven NIRS system that achieves high-accuracy, layer-specific brain oxygenation measurements, surpassing conventional methods in simulations, phantom tests, and clinical validation.

Findings

Strong correlation (R2=0.913) with actual cortical oxygenation in simulations

Superior anatomical reliability (R2=0.986) in phantom experiments

High diagnostic accuracy (AUC=0.943) in clinical validation

Abstract

Photon scattering has traditionally limited the ability of near-infrared spectroscopy (NIRS) to extract accurate, layer-specific information from the brain. This limitation restricts its clinical utility for precise neurological monitoring. To address this, we introduce an AI-driven, high-density NIRS system optimized to provide real-time, layer-specific oxygenation data from the brain cortex, specifically targeting acute neuro-emergencies. Our system integrates high-density NIRS reflectance data with a neural network trained on MRI-based synthetic datasets. This approach achieves robust cortical oxygenation accuracy across diverse anatomical variations. In simulations, our AI-assisted NIRS demonstrated a strong correlation (R2=0.913) with actual cortical oxygenation, markedly outperforming conventional methods (R2=0.469). Furthermore, biomimetic phantom experiments confirmed its superior anatomical reliability (R2=0.986) compared to standard commercial devices (R2=0.823). In clinical validation with healthy subjects and ischemic stroke patients, the system distinguished between the two groups with an AUC of 0.943. This highlights its potential as an accessible, high-accuracy diagnostic tool for emergency and point-of-care settings. These results underscore the system's capability to advance neuro-monitoring precision through AI, enabling timely, data-driven decisions in critical care environments.