Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders
Virtual disease landscape using mechanics-informed machine learning: Application to esophageal disorders
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DOI：
10.1016/j.artmed.2022.102435
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发表时间：
2022-11-15
影响因子：
7.5
通讯作者：
Patankar, Neelesh A.
中科院分区：
文献类型：
--
作者：
Halder, Sourav;Yamasaki, Jun;Patankar, Neelesh A.
食管疾病与食管壁的机械特性和功能有关。因此，为了了解各种食管疾病背后的根本机制，根据与改变的食团运输和增加的食团内压力相对应的基于力学的参数来绘制食管壁的机械行为至关重要。我们提出了一个结合流体力学和机器学习的混合框架，以识别各种食管疾病（运动障碍、嗜酸性食管炎、反流病、硬皮病食管）的基础物理学，并将它们映射到我们称为虚拟疾病景观（VDL）的参数空间上。一维逆模型处理称为功能性管腔成像探头 (FLIP) 的食管诊断设备的输出，通过预测一组基于力学的参数（例如食管壁硬度、肌肉收缩模式和食管壁主动松弛）来估计食管的机械“健康状况”。然后使用基于力学的参数来训练神经网络，该神经网络由生成潜在空间的变分自动编码器和预测机械功指标以估计食管胃连接处运动的侧网络组成。潜在向量以及一组离散的基于力学的参数定义了 VDL 并形成了与特定食管疾病相对应的簇。 VDL 不仅可以区分疾病，还可以显示疾病随时间的进展情况。最后，我们证明了该框架在评估治疗效果和跟踪治疗后患者病情方面的临床适用性。
Esophageal disorders are related to the mechanical properties and function of the esophageal wall. Therefore, to understand the underlying fundamental mechanisms behind various esophageal disorders, it is crucial to map mechanical behavior of the esophageal wall in terms of mechanics-based parameters corresponding to altered bolus transit and increased intrabolus pressure. We present a hybrid framework that combines fluid mechanics and machine learning to identify the underlying physics of various esophageal disorders (motility disorders, eosinophilic esophagitis, reflux disease, scleroderma esophagus) and maps them onto a parameter space which we call the virtual disease landscape (VDL). A one-dimensional inverse model processes the output from an esophageal diagnostic device called the functional lumen imaging probe (FLIP) to estimate the mechanical "health" of the esophagus by predicting a set of mechanics-based parameters such as esophageal wall stiffness, muscle contraction pattern and active relaxation of esophageal wall. The mechanics-based parameters were then used to train a neural network that consists of a variational autoencoder that generated a latent space and a side network that predicted mechanical work metrics for estimating esophagogastric junction motility. The latent vectors along with a set of discrete mechanics-based parameters define the VDL and formed clusters corre-sponding to specific esophageal disorders. The VDL not only distinguishes among disorders but also displayed disease progression over time. Finally, we demonstrated the clinical applicability of this framework for esti-mating the effectiveness of a treatment and tracking patients' condition after a treatment.