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Predicting Post-Dive Inert Gas Bubble Grades in Non-Decompression Scuba Diving with Air: Simplified Model for

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Even with no-decompression dives, bubbles can form. A validated formula using depth, age, and gas consumption predicts bubble grades, with machine learning offering enhanced accuracy for safer diving.

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Area of Science:

  • Physiology
  • Diving Medicine
  • Biomedical Engineering

Background:

  • No-decompression dives can still result in inert gas bubbles, increasing decompression sickness risk.
  • A prior formula predicted post-dive bubbles using individual factors and dive parameters.
  • This study validated the formula and explored detailed dive profiles for improved prediction.

Purpose of the Study:

  • Confirm the validity of a bubble prediction formula in an independent dataset.
  • Assess the relevance of detailed dive profile data for predicting bubble grades.
  • Investigate machine learning models for enhanced predictive accuracy.

Main Methods:

  • 59 divers performed 359 no-decompression air dives.
  • Post-dive transthoracic echocardiography assessed bubble grades (Eftedal-Brubakk).
  • Statistical analysis and machine learning models were applied to dive data.

Main Results:

  • Bubbles (grade ≥1) were detected in 29.8% of dives.
  • Maximum depth, dive time, air consumption, and age correlated with bubble grades.
  • Machine learning models using dive profiles showed stronger prediction (rs=0.49).

Conclusions:

  • Maximum depth, age, surface interval, and gas consumption predict post-dive bubbles in no-decompression air dives.
  • Divers can adopt bubble-reducing measures based on risk class.
  • Integrating predictive formulas into dive computers can provide real-time risk guidance.