Perchiazzi Gaetano, Giuliani Rocco, Ruggiero Loreta, Fiore Tommaso, Hedenstierna Göran
*Department of Clinical Physiology, Uppsala University Hospital, Sweden; and †Department of Emergency and Transplantation, Bari University Hospital, Italy.
Anesth Analg. 2003 Oct;97(4):1143-1148. doi: 10.1213/01.ANE.0000077905.92474.82.
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.
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.
在本研究中，我们评估了一种基于人工神经网络（ANN）的技术能否在持续机械通气期间，即使在没有吸气末暂停的情况下，估计呼吸系统的静态顺应性（C(RS)）。使用急性肺损伤猪模型来提供不同呼吸力学条件下的记录。每次记录包括在容量控制机械通气中连续10次或更多次呼吸，随后是一次有吸气末暂停的呼吸，用于根据间断技术（IT）计算C(RS)。将有暂停那次呼吸之前的那次呼吸的容积-压力环提供给人工神经网络进行训练，同时将通过间断技术单独计算的C(RS)也提供给它。前瞻性阶段包括仅将这些环提供给经过训练的人工神经网络，并将其得出的结果与研究人员单独计算的顺应性进行比较。人工神经网络和间断技术方法之间测量一致性的测定显示误差为-0.67±1.52 mL/cm H₂O（偏差±标准差）。我们可以得出结论，在容量控制机械通气期间，人工神经网络无需停止吸气气流就能提取C(RS)。
我们研究了人工神经网络（ANN）在机械通气期间估计呼吸顺应性方面的应用。该研究在急性肺损伤动物模型上进行，测试了人工神经网络在肺的健康和疾病状态下的性能。
