# Adding arterial nitrogen pressure to single‐measurement monitoring data enables diagnostic lung modeling by deep learning

## TL;DR

Adding nitrogen pressure data to blood gas analysis allows AI to accurately model lung function using a well-known physiological framework.

## Contribution

Including arterial nitrogen pressure enables deep learning to quantify West's V/Q lung model from single measurements.

## Key findings

- Deep learning predicted lung model parameters with R² ≥ 0.99 accuracy.
- Results showed strong agreement between predicted and true values for shunt, logSD, and meanV/Q.
- Sensitivity analyses confirmed the critical role of PaN2 in model accuracy.

## Abstract

We investigated whether including arterial pressure of nitrogen (PaN2) in a deep‐learning analysis of single measurements of arterial blood gases, cardiac output, and indirect calorimetry enables individualized quantification of West's ventilation/perfusion (V/Q) lung model. West's key parameters are shunt (% cardiac output supplying lung units with V/Q = 0), logSD (log standard deviation of unit V/Q ratios), and meanV/Q (mean unit V/Q ratio). By processing randomized combinations of shunt, logSD, meanV/Q, indirect calorimetry, and cardiac output data in a Python computerization of West's model, 2,010,000 blood gases including PaN2 combined with their input variables completed a simulated monitoring dataset covering broad ranges of oxygenation and acid–base equilibria. Deep‐learning applications trained on these data successfully predicted withheld values of shunt, logSD, and meanV/Q from a separate test dataset of 43,915 samples. Linear regression of predicted versus true values produced R
2 ≥ 0.99 with slopes 0.98–1.00. Kernel density estimates confirmed close agreement. Sensitivity analyses demonstrated high dependence upon PaN2. Deep‐learning analysis of single measurements of arterial blood gases, which include PaN2, when combined with cardiac output and indirect calorimetry data, can quantify individual lung function with high fidelity in terms of key parameters of West's V/Q model.