Deep Learning‐Driven Computational Imaging for Noninvasive Monitoring System of Brain Temperature and Metabolism: A Hypothermia Validation for Acute Ischemic Stroke

ABSTRACT

Brain temperature (BT) is a critical physiological indicator closely associated with neurological function and disease progression. However, real‐time, noninvasive monitoring of BT remains a challenge due to the limitations of current technologies. Here, we present a novel multimodal framework combining bioheat transfer modeling, deep learning, and computational thermography for accurate BT prediction and imaging. A one‐dimensional convolutional neural network was trained on multimodal clinical data, integrating cerebral blood flow, tissue oxygen saturation, and intracranial pressure, achieving a mean absolute error of 0.31°C in BT prediction. The framework incorporates finite element analysis to generate 3D thermographic maps of brain tissue with a spatial resolution of 0.4 mm, validated using MRI‐derived data. This approach demonstrated robust performance in predicting localized temperature variations in acute ischemic stroke patients undergoing therapeutic hypothermia, with deviations below 0.45°C. Our findings highlight the potential of this system to enable precise BT monitoring, bridging the gap between computational modeling and clinical neuro‐thermometry, and paving the way for advanced diagnostic and therapeutic interventions.