Published in ‘Nature Communications Engineering’
A new paper, authored by Intellegens scientists with collaborators from Equivital, Coventry University, and the University of Cambridge, develops a deep learning method to support physiological monitoring, accounting for the key safety-critical issue of uncertainty.
Citation Strickland J., Ghisoni M., Marshall H. et al. Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments. Commun Eng (2025). https://doi.org/10.1038/s44172-025-00548-6
DOI 10.1017/dce.2025.10018
Abstract
Accurate estimation of core body temperature (CBT) is essential for physiological monitoring, yet current non-invasive methods lack statistically calibrated uncertainty estimates required for safety-critical use. Here we introduce a conformal deep learning framework for real-time, non-invasive CBT prediction with calibrated uncertainty, demonstrated in high-risk heat-stress environments. Developed from over 140,000 physiological measurements across six operational domains, the model achieves a test error of 0.29 °C, outperforming the widely used ECTemp™ algorithm with a 12-fold improvement in calibrated probabilistic accuracy and statistically valid prediction intervals. Designed for integration with wearable devices, the system uses accessible physiological, demographic, and environmental inputs to support practical, confidence-informed monitoring. A customizable alert engine enables proactive safety interventions based on user-defined thresholds and model confidence. By combining deep learning with conformal prediction, this approach establishes a generalizable foundation for trustworthy, non-invasive physiological monitoring, demonstrated here for CBT under heat stress but applicable to broader safety-critical settings.
