Key result
Gradient-based optimization model accurately predicts decompression sickness risk with ~0.5% mean absolute error.
Why the study?
Calibration of probabilistic decompression models to quantify decompression sickness risk is challenged by the scarcity of empirical outcome data.
A gradient-based optimization model trained on existing probabilistic tables can satisfactorily predict decompression sickness risk for a given dive profile.
Decompression sickness (DCS) is a low-incidence but potentially severe consequence of hyperbaric exposure. Probabilistic decompression models offer a framework to quantify this risk, yet their calibration is challenged by the scarcity of empirical outcome data. In this study, we propose a gradient-based optimization model to predict DCS probability, trained on 924 dive profiles from the US Navy Experimental Diving Unit XVal-He-9 tables, representing predefined DCS probabilities (2.3% and 4%), and optimized based on actual body tissues grouped in five compartments. The model achieved high predictive accuracy (MAE: 0.535%; RMSE: 0.694%) with consistent performance across training and test sets, indicating limited overfitting. Reduced accuracy was observed in intermediate depth ranges (100-130fsw or 30-39msw). Out-of-sample evaluation on 31 high-risk dives (three DCS cases) showed general agreement between predicted and observed incidence while suggesting a potential contribution of repetitive exposures not accounted for in the model. These results demonstrate that gradient-based optimization, trained based on existing probabilistic tables, seems to be capable of satisfactorily predicting decompression sickness risk for a given dive profile. Additionally, future studies can further adjust the loss function to account for individual or dive-related indicators, leading to a more individualized risk function.
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Schirato et al. (2026) studied Decompression sickness (n=955). Gradient-based optimization model vs. XVal-He-9 tables was evaluated on Predictive accuracy (Mean Absolute Error and Root Mean Square Error). The gradient-based optimization model achieved high predictive accuracy for decompression sickness risk, demonstrating a mean absolute error of 0.535% and root mean square error of 0.694%.
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