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<p>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.</p>

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Degrees of uncertainty: conformal deep learning for non-invasive core body temperature prediction in extreme environments

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

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  • Joel Strickland,
  • Marco Ghisoni,
  • Hannah Marshall,
  • Thomas Whitehead,
  • Bogdan Nenchev,
  • Ben Pellegrini,
  • Charles Phillips,
  • Karl Tassenberg,
  • Sarah Davey,
  • Sandra Dorman,
  • Joseph Sol,
  • David Ferguson,
  • Gareth Conduit

摘要

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.