Assessment of respiratory system mechanics by artificial neural networks: an exploratory study.

• We evaluated 1) the performance of an artificial neural network (ANN)-based technology in assessing the respiratory system resistance (Rrs) and compliance (Crs) in a porcine model of acute lung injury and 2) the possibility of using, for ANN training, signals coming from an electrical analog (EA) of the lung. Two differently experienced ANNs were compared. One ANN (ANN(BIO)) was trained on tracings recorded at different time points after the administration of oleic acid in 10 anesthetized and paralyzed pigs during constant-flow mechanical ventilation. A second ANN (ANN(MOD)) was trained on EA simulations. Both ANNs were evaluated prospectively on data coming from four different pigs. Linear regression between ANN output and manually computed mechanics showed a regression coefficient (R) of 0.98 for both ANNs in assessing Crs. On Rrs, ANN(BIO) showed a performance expressed by R = 0.40 and ANN(MOD) by R = 0.61. These results suggest that ANNs can learn to assess the respiratory system mechanics during mechanical ventilation but that the assessment of resistance and compliance by ANNs may require different approaches.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)

Nyckelord

Animals

Lung

drug effects

pathology

physiology

Models

Biological

Neural Networks (Computer)

Oleic Acid

toxicity

Regression Analysis

Reproducibility of Results

Respiration

Artificial

Respiratory Function Tests

Respiratory Mechanics

physiology

Swine

Publikations- och innehållstyp

ref (ämneskategori)

art (ämneskategori)