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Diagnosis of obstructive sleep apnea with prediction of flow characteristics according to airway morphology automatically extracted from medical images: Computational fluid dynamics and artificial intelligence approach

Authors
 Susie Ryu  ;  Jun Hong Kim  ;  Heejin Yu  ;  Hwi-Dong Jung  ;  Suk Won Chang  ;  Jeong Jin Park  ;  Soonhyuk Hong  ;  Hyung-Ju Cho  ;  Yoon Jeong Choi  ;  Jongeun Choi  ;  Joon Sang Lee 
Citation
 COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, Vol.208 : 106243, 2021-09 
Journal Title
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN
 0169-2607 
Issue Date
2021-09
MeSH
Artificial Intelligence ; Humans ; Hydrodynamics* ; Polysomnography ; Sleep Apnea, Obstructive* / diagnostic imaging ; Trachea
Keywords
Auto-segmentation ; Computational fluid dynamics ; Obstructive sleep apnea syndrome ; Upper-airway morphology
Abstract
Background: Obstructive sleep apnea syndrome (OSAS) is being observed in an increasing number of cases. It can be diagnosed using several methods such as polysomnography. Objectives: To overcome the challenges of time and cost faced by conventional diagnostic methods, this paper proposes computational fluid dynamics (CFD) and machine-learning approaches that are derived from the upper-airway morphology with automatic segmentation using deep learning. Method: We adopted a 3D UNet deep-learning model to perform medical image segmentation. 3D UNet prevents the feature-extraction loss that may occur by concatenating layers and extracts the anteroposterior coordination and width of the airway morphology. To create flow characteristics of the upper airway training data, we analyzed the changes in flow characteristics according to the upper-airway morphology using CFD. A multivariate Gaussian process regression (MVGPR) model was used to train the flow characteristic values. The trained MVGPR enables the prompt prediction of the aerodynamic features of the upper airway without simulation. Unlike conventional regression methods, MVGPR can be trained by considering the correlation between the flow characteristics. As a diagnostic step, a support vector machine (SVM) with predicted aerodynamic and biometric features was used in this study to classify patients as healthy or suffering from moderate OSAS. SVM is beneficial as it is easy to learn even with a small dataset, and it can diagnose various flow characteristics as factors while enhancing the feature via the kernel function. As the patient dataset is small, the Monte Carlo cross-validation was used to validate the trained model. Furthermore, to overcome the imbalanced data problem, the oversampling method was applied. Result: The segmented upper-airway results of the high-resolution and low-resolution models present overall average dice coefficients of 0.76±0.041 and 0.74±0.052, respectively. Furthermore, the classification accuracy, sensitivity, specificity, and F1-score of the diagnosis algorithm were 81.5%, 89.3%, 86.2%, and 87.6%, respectively. Conclusion: The convenience and accuracy of sleep apnea diagnosis are improved using deep learning and machine learning. Further, the proposed method can aid clinicians in making appropriate decisions to evaluate the possible applications of OSAS.
Full Text
https://www.sciencedirect.com/science/article/pii/S0169260721003175
DOI
10.1016/j.cmpb.2021.106243
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Otorhinolaryngology (이비인후과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral and Maxillofacial Surgery (구강악안면외과학교실) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Orthodontics (교정과학교실) > 1. Journal Papers
Yonsei Authors
Park, Jeong Jin(박정진)
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
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184319
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