Estimating respiratory system compliance during mechanical ventilation using artificial neural networks.
In this study we evaluated whether a technology based on artificial neural networks (ANN) could estimate the static compliance (C(RS)) of the respiratory system, even in the absence of an end-inspiratory pause, during continuous mechanical ventilation. A porcine model of acute lung injury was used to provide recordings of different respiratory mechanics conditions. Each recording consisted of 10 or more consecutive breaths in volume-controlled mechanical ventilation, followed by a breath having an end-inspiratory pause used to calculate C(RS) according to the interrupter technique (IT). The volume-pressure loop of the breath immediately preceding the one with pause was given to the ANN for the training, together with the C(RS) separately calculated by the IT. The prospective phase consisted of giving only the loops to the trained ANN and comparing the results yielded by it to the compliance separately calculated by the investigators. Determination of measurement agreement between ANN and IT methods showed an error of -0.67 +/- 1.52 mL/cm H(2)O (bias +/- SD). We could conclude that ANN, during volume-controlled mechanical ventilation, can extract C(RS) without needing to stop inspiratory flow.


CONCLUSIONS
We studied the application of artificial neural networks (ANN) to the estimation of respiratory compliance during mechanical ventilation. The study was performed on an animal model of acute lung injury, testing the performance of ANN in both healthy and diseased conditions of the lung.
D000399:Air Pressure; D000769:Anesthesia, Inhalation; D000818:Animals; D001185:Artificial Intelligence; D016014:Linear Models; D008170:Lung Compliance / Q000502:physiology*; D008171:Lung Diseases / Q000503:physiopathology; D008176:Lung Volume Measurements; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012121:Respiration, Artificial; D015656:Respiratory Mechanics / Q000502:physiology; D013552:Swine