Estimation of Apnea-Hypopnea Index Using Deep Learning on 3-D Craniofacial Scans

Estimation of Apnea-Hypopnea Index Using Deep Learning on 3-D Craniofacial Scans

Obstructive sleep apnea (OSA) is characterized by decreased breathing events that occur through the night, with severity reported as the apnea-hypopnea index (AHI), which is associated with certain craniofacial features. In this study, we used data from 1366 patients collected as part of Stanford Te...

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Bibliographic Details
Main Authors: Blackman, A. (Author), Hanif, U. (Author), Jennum, P. (Author), Kushida, C.A (Author), Leary, E. (Author), Liu, S. (Author), Mignot, E. (Author), Morse, A.M (Author), Paulsen, R. (Author), Schneider, L. (Author), Schweitzer, P. (Author), Sorensen, H. (Author)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2021
Subjects:
10-fold cross-validation
actimetry
anatomic landmark
anatomical topography
Apnea
apnea hypopnea index
Apnea-hypopnea indices
area under the curve
Article
body mass
Canada
Clinical experience
computer aided design
controlled study
convolutional neural network
Convolutional neural networks
correlation coefficient
Correlation methods
craniofacial morphology
craniofacial scans
cross validation
deep learning
Deep learning
Deep Learning
Diagnosis
diagnostic imaging
diagnostic test accuracy study
Forecasting
human
Humans
major clinical study
Mean absolute error
mesh
multilayer perceptron
multi-view
Obstructive sleep apnea
Pearson correlation coefficients
polysomnography
Polysomnography
questionnaire
receiver operating characteristic
Screening tool
Sleep Apnea Syndromes
Sleep Apnea, Obstructive
sleep disordered breathing
Sleep research
support vector machine
Surveys
three-dimensional imaging
validation process
Online Access:View Fulltext in Publisher

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