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

• URL: http://arxiv.org/abs/2511.05612v1
• Date: Thu, 06 Nov 2025 13:45:01 GMT
• Title: AI-Enhanced High-Density NIRS Patch for Real-Time Brain Layer Oxygenation Monitoring in Neurological Emergencies
• Authors: Minsu Ji, Jihoon Kang, Seongkwon Yu, Jaemyoung Kim, Bumjun Koh, Jimin Lee, Guil Jeong, Jongkwan choi, Chang-Ho Yun, Hyeonmin Bae,
• Abstract summary: We introduce an AI-driven, high-density NIRS system optimized to provide real-time, layer-specific oxygenation data from the brain cortex.<n>Our system integrates high-density NIRS reflectance data with a neural network trained on MRI-based synthetic datasets.<n>In simulations, our AI-assisted NIRS demonstrated a strong correlation with actual cortical oxygenation, markedly outperforming conventional methods.
• Score: 1.554464105856087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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