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A gradient-based optimization model for predicting decompression sickness risk.

Sergio Rhein Schirato1, Massimo Pieri2, Riccardo Pelliccia2

  • 1Department of Physiology, Biosciences Institute, University of São Paulo, São Paulo, Brazil.

Frontiers in Physiology
|July 8, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a new gradient-based model to predict decompression sickness (DCS) risk using dive profiles. The model shows promising accuracy, aiding in safer hyperbaric exposures.

Area of Science:

  • Hyperbaric Medicine
  • Computational Physiology
  • Risk Assessment

Background:

  • Decompression sickness (DCS) is a severe risk of hyperbaric exposure.
  • Accurate risk prediction is limited by scarce empirical data.
  • Existing probabilistic models require improved calibration.

Purpose of the Study:

  • To develop and validate a gradient-based optimization model for predicting DCS probability.
  • To train the model using established dive tables and compartmentalized body tissue data.
  • To assess the model's predictive accuracy and identify limitations.

Main Methods:

  • Utilized 924 dive profiles from US Navy Experimental Diving Unit XVal-He-9 tables.
  • Employed a gradient-based optimization approach with five-compartment body tissue modeling.
Keywords:
SCUBAdecompression sicknesshyperbaric environmentoptimizations algorithmsprobabilistic models

Related Experiment Videos

  • Evaluated model performance using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

    • Main Results:

    • Achieved high predictive accuracy (MAE: 0.535%; RMSE: 0.694%) with minimal overfitting.
    • Observed reduced accuracy in intermediate depth ranges (100-130 fsw).
    • Out-of-sample evaluation showed general agreement with observed DCS cases, suggesting potential for repetitive exposure factors.

    Conclusions:

    • Gradient-based optimization effectively predicts decompression sickness risk from dive profiles.
    • The model provides a satisfactory tool for assessing DCS risk based on existing data.
    • Future refinements could incorporate individual and dive-specific factors for personalized risk assessment.