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

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