Perchiazzi G, Högman M, Rylander C, Giuliani R, Fiore T, Hedenstierna G
Department of Emergency and Transplantation, Bari University Hospital, 70124 Bari, Italy.
J Appl Physiol (1985). 2001 May;90(5):1817-24. doi: 10.1152/jappl.2001.90.5.1817.
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.
1）基于人工神经网络（ANN）的技术在急性肺损伤猪模型中评估呼吸系统阻力（Rrs）和顺应性（Crs）的性能；2）使用来自肺电模拟（EA）的信号进行ANN训练的可能性。比较了两个经验不同的人工神经网络。一个人工神经网络（ANN(BIO)）基于在10只麻醉并麻痹的猪中，于恒流机械通气期间给予油酸后不同时间点记录的描记图进行训练。第二个人工神经网络（ANN(MOD)）基于电模拟（EA）仿真进行训练。两个神经网络均对来自四只不同猪的数据进行前瞻性评估。在评估Crs时，ANN输出与手动计算的力学之间的线性回归显示，两个神经网络的回归系数（R）均为0.98。在Rrs方面，ANN(BIO)的表现为R = 0.40，ANN(MOD)的表现为R = 0.61。这些结果表明，人工神经网络可以学会在机械通气期间评估呼吸系统力学，但人工神经网络对阻力和顺应性的评估可能需要不同的方法。
