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J Appl Physiol 90: 1817-1824, 2001;
8750-7587/01 $5.00
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Vol. 90, Issue 5, 1817-1824, May 2001

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

Gaetano Perchiazzi1,2, Marieann Högman2, Christian Rylander3, Rocco Giuliani1, Tommaso Fiore1, and Göran Hedenstierna2

1 Department of Emergency and Transplantation, Bari University Hospital, 70124 Bari, Italy; 2 Department of Clinical Physiology, Uppsala University Hospital, S-75185 Uppsala, Sweden; and 3 Department of Anaesthesia, Sahlgrenska University Hospital, S-41345 Göteborg, Sweden

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 (ANNBIO) 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 (ANNMOD) 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, ANNBIO showed a performance expressed by R = 0.40 and ANNMOD 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.

resistance; compliance; oleic acid; acute lung injury


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