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Simulation-Guided Deep Learning for Target-Layer Oxygenation Inference from NIRS Signals
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- Authors
- Jinho Park, Dohum Kim, Thien Nguyen, Amir Gandjbakhche
- Issue Date
- 2026-07
- Citation
- International Conference on Intelligent Systems for Molecular Biology (ISMB) 2026, pp.1-1
- Publisher
- International Society for Computational Biology (ISCB)
- Language
- English
- Type
- Conference Paper
- Abstract
- Accurate estimation of deep tissue oxygenation is essential for understanding physiological processes and assessing conditions such as placental dysfunction. However, NIRS signals consist of mixed contributions from multiple tissue layers, making it challenging to accurately infer oxygenation of a specific target tissue layer. We propose a simulation-guided deep learning framework to infer oxygen saturation in a target tissue layer from multi-wavelength, multi-distance NIRS measurements. A two-layer tissue model was constructed to represent superficial and deep biological structures, and Monte Carlo simulations were used to generate a large-scale dataset under varying tissue thicknesses and oxygenation conditions, incorporating a multi-distance source–detector configuration (1–6 cm separations) and three wavelengths (730, 800, and 850 nm) to reflect realistic measurement settings. A one-dimensional convolutional neural network (1D-CNN) was trained to estimate target-layer oxygenation from optical measurements and superficial layer thickness. The model was trained using an adaptive optimization method and mean squared error loss, effectively capturing the nonlinear relationship between mixed optical signals and underlying physiological parameters, and demonstrating accurate and robust prediction performance across varying tissue conditions. This study shows that integrating physics-based simulation with data-driven modeling enables target-specific, depth-resolved inference of tissue oxygenation from non-invasive measurements. The proposed approach provides a scalable framework for solving inverse problems in biological signal analysis and has potential applications in non-invasive monitoring of placental oxygenation and other clinically relevant physiological processes.
- KSP Keywords
- 850 nm, Adaptive optimization, Biological signal analysis, Biological structures, Convolution neural network(CNN), Data-driven modeling, Deep learning framework, Deep tissue, Large-scale datasets, Monte-Carlo simulation(MCS), Non-linear relationship