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Computational analysis of airflow dynamics for predicting collapsible sites in the upper airways: machine learning approach

 Seung Ho Yeom  ;  Ji Sung Na  ;  wi-Dong Jung  ;  Hyung-Ju Cho  ;  Yoon Jeong Choi  ;  Joon Sang Lee 
 Journal of Applied Physiology, Vol.127(4) : 959-973, 2019 
Journal Title
 Journal of Applied Physiology 
Issue Date
machine learning ; numerical simulation ; obstructive sleep apnea ; upper airway
Obstructive sleep apnea (OSA) is a common sleep breathing disorder. With the use of 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, 4 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 data set obtained through numerical method. The results demonstrated that the mean accuracy of the overall GPR model was ~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 and severe. We achieved an accuracy of 82.5% with the training data set and an accuracy of 80% with the test data set.NEW & NOTEWORTHY There have been many studies on the analysis of obstructive sleep apnea (OSA) through computational fluid dynamics and finite element analysis. However, these methods are not useful for practical medical applications because they have limited information for OSA symptom. This study employs the machine learning algorithm to predict flow characteristics quickly and to determine the symptoms of the patient's OSA. The overall Gaussian process regression model's mean accuracy was ~72%, and the accuracy for the classification of OSA was >80%.
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Appears in Collections:
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Jung, Hwi Dong(정휘동) ORCID logo https://orcid.org/0000-0003-1025-8323
Cho, Hyung Ju(조형주) ORCID logo https://orcid.org/0000-0002-2851-3225
Choi, Yoon Jeong(최윤정) ORCID logo https://orcid.org/0000-0003-0781-8836
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