Innovations in Decompression Sickness Prediction and Adaptive Ascent Algorithms

Abstract

Decompression Sickness (DCS) is a potentially serious medical condition which can occur in humans when there is a decrease in ambient pressure. While it is generally accepted that DCS is initiated by the formation and growth of inert gas bubbles in the body, the mechanisms of its various forms are not completely understood. Complicating matters, divers often face challenges in adhering to predetermined safe ascent paths due to unpredictable environmental conditions. Therefore, the challenge of improving dive safety is twofold: 1) enhancing the accuracy of models in predicting DCS risk for a given dive profile; 2) developing algorithms, recommending safe ascent profiles, and capable of adapting in real time to new unforeseen diving conditions. This dissertation addresses both problems in the context of diving applications.First, we examine how the DCS risk is partitioned in air decompression dives to identify which portion of the dive is the most challenging. Our findings show that most of the risk might be accrued at surface, or during the ascent phase, depending on the specific mission parameters. Subsequently, we conducted a comprehensive investigation into DCS models incorporating inter-tissue perfusion dynamics. We proposed a novel algorithm to optimize these models efficiently. Our results determined that a model neglecting the coupling of faster tissue to slower tissues outperformed all other models on O2 surface decompression dive profiles. We further conducted experiments with various compartment tissue connections, involving diffusion phenomena and introducing delayed dynamics, while also exploring different risk functions. By adopting the Akaike Information Criterion, we found that the best performing model on the training set was BQE22AXT4, a four-compartment model featuring a risk threshold term only in the fourth compartment. Conversely, the classical Linear-Exponential model demonstrated superior performance on the extrapolation set. Finally, we introduce a groundbreaking real-time algorithm that delivers a secure and time optimized ascent path capable of adapting to unanticipated conditions. Our approach harnesses the power of advanced machine learning techniques and backward optimal control. Through our comprehensive analysis, we demonstrate that this innovative methodology attains a safety level on par with precomputed NAVY tables, while offering the added advantage of dynamic adaptation in response to unexpected events.