Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: Machine learning approach.
Authors of this article are:
Yeom SH, Na JS, Jung HD, Cho HJ, Choi YJ, Lee JS.
A summary of the article is shown below:
Obstructive sleep apnea (OSA) is a common sleep breathing disorder. Employing computational fluid dynamics (CFD), this study provides a quantitative standard for accurate diagnosis and effective surgery based on the investigation of the relationship between airway geometry and aerodynamic characteristics. Based on computed tomography data from patients having normal geometry, four major geometric parameters were selected, and a total of 160 idealized cases were modeled and simulated. We created a predictive model using Gaussian process regression (GPR) through a dataset obtained through numerical method. The results demonstrated that the mean accuracy of the overall GPR model was approximately 72% with respect to the CFD results for the realistic upper airway model. A support vector machine model was also used to identify the degree of OSA symptoms in patients as normal-mild and moderate/severe. We achieved an accuracy of 82.5% with the training dataset and an accuracy of 80% with the test dataset.
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This article is a good source of information and a good way to become familiar with topics such as: Obstructive sleep apnea;machine learning;numerical simulation;upper airway.
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