Diagnostic Performance of Machine Learning-Derived Obstructive Sleep Apnea Prediction Tools in Large Clinical and Community-based Samples.

Abstract Introduction Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder with an estimation of one billion people. Full-night polysomnography is considered the gold standard for OSA diagnosis. However, it is time-consuming, expensive and is not readily available in many parts of the world. Many screening questionnaires and scores have been proposed for OSA prediction with high sensitivity and low specificity. The present study is intended to develop models with various machine learning techniques to predict the severity of OSA by incorporating features from multiple questionnaires. Methods Subjects who underwent full-night polysomnography in Torr sleep center, Texas and completed 5 OSA screening questionnaires/scores were included. OSA was diagnosed by using Apnea-Hypopnea Index ≥ 5. We trained five different machine learning models including Deep Neural Networks with the scaled principal component analysis (DNN-PCA), Random Forest (RF), Adaptive Boosting classifier (ABC), and K-Nearest Neighbors classifier (KNC) and Support Vector Machine Classifier (SVMC). Training:Testing subject ratio of 65:35 was used. All features including demographic data, body measurement, snoring and sleepiness history were obtained from 5 OSA screening questionnaires/scores (STOP-BANG questionnaires, Berlin questionnaires, NoSAS score, NAMES score and No-Apnea score). Performance parametrics were used to compare between machine learning models. Results Of 180 subjects, 51.5 % of subjects were male with mean (SD) age of 53.6 (15.1). One hundred and nineteen subjects were diagnosed with OSA. Area Under the Receiver Operating Characteristic Curve (AUROC) of DNN-PCA, RF, ABC, KNC, SVMC, STOP-BANG questionnaire, Berlin questionnaire, NoSAS score, NAMES score, and No-Apnea score were 0.85, 0.68, 0.52, 0.74, 0.75, 0.61, 0.63, 0,61, 0.58 and 0,58 respectively. DNN-PCA showed the highest AUROC with sensitivity of 0.79, specificity of 0.67, positive-predictivity of 0.93, F1 score of 0.86, and accuracy of 0.77. Conclusion Our result showed that DNN-PCA outperforms OSA screening questionnaires, scores and other machine learning models. Support (if any):

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Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study

Sleep And Breathing ◽

10.1007/s11325-021-02301-7 ◽

2021 ◽

Author(s):

Satoru Tsuiki ◽

Takuya Nagaoka ◽

Tatsuya Fukuda ◽

Yuki Sakamoto ◽

Fernanda R. Almeida ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Convolutional Neural Network ◽

Deep Convolutional Neural Network ◽

Apnea Hypopnea Index ◽

Cephalometric Analysis ◽

Obstructive Sleep ◽

Main Region

Abstract Purpose In 2-dimensional lateral cephalometric radiographs, patients with severe obstructive sleep apnea (OSA) exhibit a more crowded oropharynx in comparison with non-OSA. We tested the hypothesis that machine learning, an application of artificial intelligence (AI), could be used to detect patients with severe OSA based on 2-dimensional images. Methods A deep convolutional neural network was developed (n = 1258; 90%) and tested (n = 131; 10%) using data from 1389 (100%) lateral cephalometric radiographs obtained from individuals diagnosed with severe OSA (n = 867; apnea hypopnea index > 30 events/h sleep) or non-OSA (n = 522; apnea hypopnea index < 5 events/h sleep) at a single center for sleep disorders. Three kinds of data sets were prepared by changing the area of interest using a single image: the original image without any modification (full image), an image containing a facial profile, upper airway, and craniofacial soft/hard tissues (main region), and an image containing part of the occipital region (head only). A radiologist also performed a conventional manual cephalometric analysis of the full image for comparison. Results The sensitivity/specificity was 0.87/0.82 for full image, 0.88/0.75 for main region, 0.71/0.63 for head only, and 0.54/0.80 for the manual analysis. The area under the receiver-operating characteristic curve was the highest for main region 0.92, for full image 0.89, for head only 0.70, and for manual cephalometric analysis 0.75. Conclusions A deep convolutional neural network identified individuals with severe OSA with high accuracy. Future research on this concept using AI and images can be further encouraged when discussing triage of OSA.

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419 STOP-Bang Score and History of Radiation Predicts Risk of Obstructive Sleep Apnea in Cancer Patients: A Machine Learning Study

SLEEP ◽

10.1093/sleep/zsab072.418 ◽

2021 ◽

Vol 44 (Supplement_2) ◽

pp. A166-A166

Author(s):

Ankita Paul ◽

Karen Wong ◽

Anup Das ◽

Diane Lim ◽

Miranda Tan

Keyword(s):

Machine Learning ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cancer Patients ◽

Prediction Accuracy ◽

Screening Tools ◽

Cancer Type ◽

Sleep Study ◽

Obstructive Sleep ◽

History Of

Abstract Introduction Cancer patients are at an increased risk of moderate-to-severe obstructive sleep apnea (OSA). The STOP-Bang score is a commonly used screening questionnaire to assess risk of OSA in the general population. We hypothesize that cancer-relevant features, like radiation therapy (RT), may be used to determine the risk of OSA in cancer patients. Machine learning (ML) with non-parametric regression is applied to increase the prediction accuracy of OSA risk. Methods Ten features namely STOP-Bang score, history of RT to the head/neck/thorax, cancer type, cancer stage, metastasis, hypertension, diabetes, asthma, COPD, and chronic kidney disease were extracted from a database of cancer patients with a sleep study. The ML technique, K-Nearest-Neighbor (KNN), with a range of k values (5 to 20), was chosen because, unlike Logistic Regression (LR), KNN is not presumptive of data distribution and mapping function, and supports non-linear relationships among features. A correlation heatmap was computed to identify features having high correlation with OSA. Principal Component Analysis (PCA) was performed on the correlated features and then KNN was applied on the components to predict the risk of OSA. Receiver Operating Characteristic (ROC) - Area Under Curve (AUC) and Precision-Recall curves were computed to compare and validate performance for different test sets and majority class scenarios. Results In our cohort of 174 cancer patients, the accuracy in determining OSA among cancer patients using STOP-Bang score was 82.3% (LR) and 90.69% (KNN) but reduced to 89.9% in KNN using all 10 features mentioned above. PCA + KNN application using STOP-Bang score and RT as features, increased prediction accuracy to 94.1%. We validated our ML approach using a separate cohort of 20 cancer patients; the accuracies in OSA prediction were 85.57% (LR), 91.1% (KNN), and 92.8% (PCA + KNN). Conclusion STOP-Bang score and history of RT can be useful to predict risk of OSA in cancer patients with the PCA + KNN approach. This ML technique can refine screening tools to improve prediction accuracy of OSA in cancer patients. Larger studies investigating additional features using ML may improve OSA screening accuracy in various populations Support (if any):

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Obstructive Sleep Apnea (OSA) Events Classification by Effective Radar Cross Section (ERCS) Method Using Microwave Doppler Radar and Machine Learning Classifier

2020 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) ◽

10.1109/imbioc47321.2020.9385028 ◽

2020 ◽

Author(s):

Farjana Snigdha ◽

Shekh M M Islam ◽

Olga Boric-Lubecke ◽

Victor Lubecke

Keyword(s):

Machine Learning ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Cross Section ◽

Doppler Radar ◽

Radar Cross Section ◽

Learning Classifier ◽

Obstructive Sleep

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Effects of adenotonsillectomy on plasma inflammatory biomarkers in obese children with obstructive sleep apnea: A community-based study

International Journal of Obesity ◽

10.1038/ijo.2015.37 ◽

2015 ◽

Vol 39 (7) ◽

pp. 1094-1100 ◽

Cited By ~ 33

Author(s):

L Kheirandish-Gozal ◽

A Gileles-Hillel ◽

M L Alonso-Álvarez ◽

E Peris ◽

R Bhattacharjee ◽

...

Keyword(s):

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Inflammatory Biomarkers ◽

Obese Children ◽

Community Based ◽

Obstructive Sleep

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Obstructive sleep apnea (OSA) in a community based heart failure program: prevalence and risk factors

Journal of Cardiac Failure ◽

10.1016/j.cardfail.2004.06.362 ◽

2004 ◽

Vol 10 (4) ◽

pp. S115

Author(s):

Amber M. Shah ◽

Lee R. Goldberg ◽

Rene J. Alvarez

Keyword(s):

Risk Factors ◽

Heart Failure ◽

Obstructive Sleep Apnea ◽

Sleep Apnea ◽

Community Based ◽

Obstructive Sleep

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Association of obstructive sleep apnea with microvascular endothelial dysfunction and subclinical coronary artery disease in a community-based population

Vascular Medicine ◽

10.1177/1358863x18755003 ◽

2018 ◽

Vol 23 (4) ◽

pp. 331-339 ◽

Cited By ~ 10

Author(s):

Daniel Shpilsky ◽

Sebhat Erqou ◽

Sanjay R Patel ◽

Kevin E Kip ◽

Oluremi Ajala ◽

...

Keyword(s):

Coronary Artery Disease ◽

Obstructive Sleep Apnea ◽

Coronary Artery ◽

Sleep Apnea ◽

Endothelial Dysfunction ◽

Community Based ◽

Black And White ◽

Obstructive Sleep ◽

Artery Disease ◽

The Impact

Studies have reported an association between obstructive sleep apnea (OSA) and cardiovascular disease (CVD) morbidity and mortality. Proposed mechanisms include endothelial dysfunction and atherosclerosis. We aimed to investigate the associations of OSA with endothelial dysfunction and subclinical atherosclerotic coronary artery disease (CAD), and assess the impact of race on these associations. We used data from the Heart Strategies Concentrating on Risk Evaluation (Heart SCORE) study, a community-based prospective cohort with approximately equal representation of black and white participants. OSA severity was measured in 765 individuals using the apnea-hypopnea index (AHI). Endothelial dysfunction was measured using the Endo-PAT device, expressed as Framingham reactive hyperemia index (F_RHI). Coronary artery calcium (CAC), a marker of subclinical CAD, was quantified by electron beam computed tomography. There were 498 (65%) female participants, 282 (37%) black individuals, and 204 (26%) participants with moderate/severe OSA (AHI ≥15). In univariate models, moderate/severe OSA was associated with lower F_RHI and higher CAC, as well as several traditional CVD risk factors including older age, male sex, hypertension, diabetes, higher body mass index, and lower high-density lipoprotein cholesterol levels. In a multivariable model, individuals with moderate/severe OSA had 10% lower F_RHI and 35% higher CAC, which did not reach statistical significance ( p=0.08 for both comparisons). There was no significant interaction of race on the association of OSA with F_RHI or CAC ( p-value >0.1 for all comparisons). In a community-based cohort comprised of black and white participants, moderate/severe OSA was modestly associated with endothelial dysfunction and subclinical atherosclerotic CAD. These associations did not vary by race.

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