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

Hanif, Umaer; Leary, Eileen; Schneider, Logan; Paulsen, Rasmus Reinhold; Morse, Anne Marie; Blackman, Adam; Schweitzer, Paula K.; Kushida, Clete A.; Liu, Stanley Yung‐Chuan; Jennum, Poul; Sørensen, Helge Bjarup Dissing; Mignot, Emmanuel

doi:10.1109/jbhi.2021.3078127

Cited by 21 publications

(11 citation statements)

References 52 publications

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“…The scans were subsequently rendered and rotated in 45-degree increments to generate 2D images and depth maps that were fed into a convolutional neural network to predict the AHI values from the PSG. The mean absolute error of the proposed model was 11.38 events/hour, with an accuracy of 67% (39). The drawback of this prediction method is that it requires the facial acquisition of individual patients and needs to be calibrated and uploaded, which makes it difficult to collect patient data.…”

Section: Discussionmentioning

confidence: 99%

“…He et al ( 21 ) reported the best results thus far using frontal, 45-degree lateral, and 90-degree lateral images to predict OSA using ConvNet neural network models and could attain 91–95% sensitivity and 73–80% specificity. Hanif et al ( 39 ) detected predefined facial landmarks and aligned the scans in 3D space. The scans were subsequently rendered and rotated in 45-degree increments to generate 2D images and depth maps that were fed into a convolutional neural network to predict the AHI values from the PSG.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Zhang¹,

Feng²,

Li³

et al. 2023

J Thorac Dis

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Background: Obstructive sleep apnea (OSA) is a common sleep disorder. However, current diagnostic methods are labor-intensive and require professionally trained personnel. We aimed to develop a deep learning model using upper airway computed tomography (CT) to predict OSA and to warn the medical technician if a patient has OSA while the patient is undergoing any head and neck CT scan, even for other diseases.Methods: A total of 219 patients with OSA [apnea-hypopnea index (AHI) ≥10/h] and 81 controls (AHI <10/h) were enrolled. We reconstructed each patient's CT into 3 types (skeletal structures, external skin structures, and airway structures) and captured reconstructed models in 6 directions (front, back, top, bottom, left profile, and right profile). The 6 images from each patient were imported into the ResNet-18 network to extract features and output the probability of OSA using two fusion methods: Add and Concat.Five-fold cross-validation was used to reduce bias. Finally, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.Results: All 18 views with Add as the feature fusion performed better than did the other reconstruction and fusion methods. This gave the best performance for this prediction method with an AUC of 0.882. Conclusions:We present a model for predicting OSA using upper airway CT and deep learning. The model has satisfactory performance and enables CT to accurately identify patients with moderate to severe OSA.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Zhang¹,

Feng²,

Li³

et al. 2023

J Thorac Dis

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“…In addition, a relevant study developed machine learning approaches for OSA severity classification based on scanned craniofacial feature images, but the accuracy of those approaches was 67% for predicting the risk of moderate-to-severe OSA. 57 Variations in obtained craniofacial images (due to breathing movements or muscle tone) may have affected the accuracy. Also, OSA presentation might not completely be caused by craniofacial factors.…”

Section: Discussionmentioning

confidence: 99%

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

et al. 2023

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Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.

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“…The Stanford Technology Analytics and Genomics in Sleep (STAGES) study, described previously [ 38 , 39 ], collected data from patients across 11 different sleep clinics between 2018 and 2020. Briefly, all participants were patients who attended an appointment with a physician at a sleep clinic and completed an overnight polysomnography (PSG) study, in addition to completing the Alliance Sleep Questionnaire (ASQ) and providing a blood sample.…”

Section: Methodsmentioning

confidence: 99%

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response

Cederberg

Hanif

Sempere

et al. 2022

IJMS

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Obstructive sleep apnea (OSA), a disease associated with excessive sleepiness and increased cardiovascular risk, affects an estimated 1 billion people worldwide. The present study examined proteomic biomarkers indicative of presence, severity, and treatment response in OSA. Participants (n = 1391) of the Stanford Technology Analytics and Genomics in Sleep study had blood collected and completed an overnight polysomnography for scoring the apnea–hypopnea index (AHI). A highly multiplexed aptamer-based array (SomaScan) was used to quantify 5000 proteins in all plasma samples. Two separate intervention-based cohorts with sleep apnea (n = 41) provided samples pre- and post-continuous/positive airway pressure (CPAP/PAP). Multivariate analyses identified 84 proteins (47 positively, 37 negatively) associated with AHI after correction for multiple testing. Of the top 15 features from a machine learning classifier for AHI ≥ 15 vs. AHI < 15 (Area Under the Curve (AUC) = 0.74), 8 were significant markers of both AHI and OSA from multivariate analyses. Exploration of pre- and post-intervention analysis identified 5 of the 84 proteins to be significantly decreased following CPAP/PAP treatment, with pathways involving endothelial function, blood coagulation, and inflammatory response. The present study identified PAI-1, tPA, and sE-Selectin as key biomarkers and suggests that endothelial dysfunction and increased coagulopathy are important consequences of OSA, which may explain the association with cardiovascular disease and stroke.

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

Cited by 21 publications

References 52 publications

Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Prediction of obstructive sleep apnea using deep learning in 3D craniofacial reconstruction

Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events

Proteomic Biomarkers of the Apnea Hypopnea Index and Obstructive Sleep Apnea: Insights into the Pathophysiology of Presence, Severity, and Treatment Response

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