Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study
Neural Network-based Estimation of Microbubbles Generated in Cardiopulmonary Bypass Circuit: A Clinical Application Study
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基于神经网络的心肺旁路回路中产生的微泡估计：临床应用研究
DOI：
10.1109/embc48229.2022.9871662
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Tsuji Toshio
中科院分区：
文献类型：
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作者：
Miyamoto Satoshi;Soh Zu;Okahara Shigeyuki;Furui Akira;Takasaki Taiichi;Katayama Keijiro;Takahashi Shinya;Tsuji Toshio
心脏手术中使用的心肺旁路系统会产生微泡（MB），当微泡进入血管时可能会导致并发症，例如神经认知功能障碍。因此，有必要估计生成的 MB 数量，以便外科医生能够处理它。为此，我们之前提出了一种基于神经网络的模型，用于根据心肺旁路系统可测量的四个因素来估计MB的数量：抽吸流速、静脉储液器水平、血液粘度和灌注流速。然而，该模型尚未适应从实际手术病例收集的数据。在本研究中，在四个临床病例中检查了所提出的模型估计的 MB 的准确性。结果表明，整个手术过程中估计的 MB 与测量的 MB 之间的决定系数为 R2=0.558（p<0.001）。我们发现手术治疗，例如给药、液体和输血，增加了测量到的 MB 数量。通过排除这些治疗的持续时间，决定系数增加至 R2= 0.8762 (p<0.001)。这一结果表明该模型可以在临床环境下高精度地估计MB的数量。
The cardiopulmonary bypass system used in cardiac surgery can generate microbubbles (MBs) that may cause complications, such as neurocognitive dysfunction, when delivered into the blood vessel. Estimating the number of MBs generated, thus, is necessary to enable the surgeons to deal with it. To this end, we previously proposed a neural network-based model for estimating the number of MBs from four factors measurable from the cardiopulmonary bypass system: suction flow rate, venous reservoir level, blood viscosity, and perfusion flow rate. However, the model has not been adapted to the data collected from actual surgery cases. In this study, the accuracy of MBs estimated by the proposed model was examined in four clinical cases. The results showed that the coefficient of determination between estimated MBs and the measured MBs throughout the surgeries was R2=0.558 (p<0.001). We found that the surgical treatments, such as administration of drugs, fluids and blood transfusions, increased the number of measured MBs. The coefficient of determination increased to R2= 0.8762 (p<0.001) by excluding the duration of these treatments. This result indicates that the model can estimate the number of MBs with high accuracy under the clinical environment.