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

Seung Ho Yeom, Ji Sung Na, Hwi Dong Jung, Hyung Ju Cho, Yoon Jeong Choi, Joon Sang Lee

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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%.

Original languageEnglish
Pages (from-to)959-973
Number of pages15
JournalJournal of Applied Physiology
Issue number4
Publication statusPublished - 2019

Bibliographical note

Funding Information:
This work was supported by National Research Foundation of Korea (NRF) Grant 2015R1A5A1037668, NRF-2017M3A9E9073371 funded by the Korean Government (Ministry of Science, ICT, and Future Planning).

Publisher Copyright:
Copyright © 2019 the American Physiological Society.

All Science Journal Classification (ASJC) codes

  • Physiology
  • Physiology (medical)


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