Diagnosis of Obstructive Sleep Apnea in Adults Using the Cardiopulmonary Coupling-Derived Software-Generated Apnea-Hypopnea Index

Automatic prediction of obstructive sleep apnea in patients with temporomandibular disorder based on multidata and machine learning

Obstructive sleep apnea (OSA) is closely associated with the development and chronicity of temporomandibular disorder (TMD). Given the intricate pathophysiology of both OSA and TMD, comprehensive diagnostic approaches are crucial. This study aimed to develop an automatic prediction model utilizing multimodal data to diagnose OSA among TMD patients. We collected a range of multimodal data, including clinical characteristics, portable polysomnography, X-ray, and MRI data, from 55 TMD patients who reported sleep problems. This data was then analyzed using advanced machine learning techniques. Three-dimensional VGG16 and logistic regression models were used to identify significant predictors. Approximately 53% (29 out of 55) of TMD patients had OSA. Performance accuracy was evaluated using logistic regression, multilayer perceptron, and area under the curve (AUC) scores. OSA prediction accuracy in TMD patients was 80.00-91.43%. When MRI data were added to the algorithm, the AUC score increased to 1.00, indicating excellent capability. Only the obstructive apnea index was statistically significant in predicting OSA in TMD patients, with a threshold of 4.25 events/h. The learned features of the convolutional neural network were visualized as a heatmap using a gradient-weighted class activation mapping algorithm, revealing that it focuses on differential anatomical parameters depending on the absence or presence of OSA. In OSA-positive cases, the nasopharynx, oropharynx, uvula, larynx, epiglottis, and brain region were recognized, whereas in OSA-negative cases, the tongue, nose, nasal turbinate, and hyoid bone were recognized. Prediction accuracy and heat map analyses support the plausibility and usefulness of this artificial intelligence-based OSA diagnosis and prediction model in TMD patients, providing a deeper understanding of regions distinguishing between OSA and non-OSA.

Screening of preoperative obstructive sleep apnea by cardiopulmonary coupling and its risk factors in patients with plans to receive surgery under general anesthesia: a cross-sectional study

Objective

Preoperative obstructive sleep apnea (OSA) is supposed to be the abnormally high occurrence of OSA the night before surgery under general anesthesia. This study aimed to evaluate the prevalence preoperative OSA using cardiopulmonary coupling (CPC) and its correlation with imbalance of sympathetic/parasympathetic nervous system.

Methods

A total of 550 patients with plans to receive surgery under general anesthesia were enrolled. All patients were assigned to wear CPC on the night before surgery until the next day. Sleep quality characteristics, heart rate variation parameters, and apnea-hypopnea index were acquired. The diagnosis of pre-existing OSA was not considered in the current study.

Results

According to apnea-hypopnea index, 28.4%, 32.2%, 26.2%, and 13.3% patients were assessed as no, mild, moderate, and severe operative OSA, respectively. Multivariate logistic regression model revealed that higher age [p < 0.001, odds ratio (OR) = 1.043] was independently and positively associated with preoperative OSA; heart rate variation parameters representing the imbalance of sympathetic/parasympathetic nervous system, such as higher low-frequency (p < 0.001, OR = 1.004), higher low-frequency/high-frequency ratio (p = 0.028, OR = 1.738), lower NN20 count divided by the total number of all NN intervals (pNN20; p < 0.001, OR = 0.950), and lower high-frequency (p < 0.001, OR = 0.998), showed independent relationships with a higher probability of preoperative OSA. Higher age (p = 0.005, OR = 1.024), higher very-low-frequency (p < 0.001, OR = 1.001), and higher low-frequency/high-frequency ratio (p = 0.003, OR = 1.655) were associated with a higher probability of moderate-to-severe preoperative OSA, but higher pNN10 (p < 0.001, OR = 0.951) was associated with a lower probability of moderate-to-severe preoperative OSA.

Conclusion

Preoperative OSA is prevalent. Higher age and imbalance of sympathetic/parasympathetic nervous system are independently and positively associated with a higher occurrence of preoperative OSA. CPC screening may promote the management of preoperative OSA.

Comparative study of the SleepImage ring device and polysomnography for diagnosing obstructive sleep apnea

Purpose We aim to evaluate the diagnostic performance of the SleepImage Ring device in identifying obstructive sleep apnea (OSA) across different severity in comparison to standard polysomnography (PSG). Methods Thirty-nine patients (mean age, 56.8 ± 15.0 years; 29 [74.3%] males) were measured with the SleepImage Ring and PSG study simultaneously in order to evaluate the diagnostic performance of the SleepImage device for diagnosing OSA. Variables such as sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive value, and accuracy were calculated with PSG-AHI thresholds of 5, 15, and 30 events/h. Receiver operating characteristic curves were also built according to the above PSG-AHI thresholds. In addition, we analyzed the correlation and agreement between the apnea-hypopnea index (AHI) obtained from the two measurement devices. Results There was a strong correlation (r = 0.89, P < 0.001 and high agreement in AHI between the SleepImage Ring and standard PSG. Also, the SleepImage Ring showed reliable diagnostic capability, with areas under the receiver operating characteristic curve of 1.00 (95% CI, 0.91, 1.00), 0.90 (95% CI, 0.77, 0.97), and 0.98 (95% CI, 0.88, 1.000) for corresponding PSG-AHI of 5, 15 and 30 events/h, respectively. Conclusion The SleepImage Ring could be a clinically reliable and cheaper alternative to the gold standard PSG when aiming to diagnose OSA in adults.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13534-023-00304-9.

Craniofacial Sleep Medicine: The Important Role of Dental Providers in Detecting and Treating Sleep Disordered Breathing in Children

Obstructive sleep apnea (OSA) is a clinical disorder within the spectrum of sleep-related breathing disorders (SRDB) which is used to describe abnormal breathing during sleep resulting in gas exchange abnormalities and/or sleep disruption. OSA is a highly prevalent disorder with associated sequelae across multiple physical domains, overlapping with other chronic diseases, affecting development in children as well as increased health care utilization. More precise and personalized approaches are required to treat the complex constellation of symptoms with its associated comorbidities since not all children are cured by surgery (removal of the adenoids and tonsils). Given that dentists manage the teeth throughout the lifespan and have an important understanding of the anatomy and physiology involved with the airway from a dental perspective, it seems reasonable that better understanding and management from their field will give the opportunity to provide better integrated and optimized outcomes for children affected by OSA. With the emergence of therapies such as mandibular advancement devices and maxillary expansion, etc., dentists can be involved in providing care for OSA along with sleep medicine doctors. Furthermore, the evolving role of myofunctional therapy may also be indicated as adjunctive therapy in the management of children with OSA. The objective of this article is to discuss the important role of dentists and the collaborative approach between dentists, allied dental professionals such as myofunctional therapists, and sleep medicine specialists for identifying and managing children with OSA. Prevention and anticipatory guidance will also be addressed.

Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea

Background

Obstructive sleep apnea is a common clinical condition and has a significant impact on the health of patients if untreated. The current diagnostic gold standard for obstructive sleep apnea is polysomnography, which is labor intensive, requires specialists to utilize, expensive, and has accessibility challenges. There are also challenges with awareness and identification of obstructive sleep apnea in the primary care setting. Artificial intelligence systems offer the opportunity for a new diagnostic approach that addresses the limitations of polysomnography and ultimately benefits patients by streamlining the diagnostic expedition.

Main body

The purpose of this project is to elucidate the barriers that exist in the implementation of artificial intelligence systems into the diagnostic context of obstructive sleep apnea. It is essential to understand these challenges in order to proactively create solutions and establish an efficient adoption of this new technology. The literature regarding the evolution of the diagnosis of obstructive sleep apnea, the role of artificial intelligence in the diagnosis, and the barriers in artificial intelligence implementation was reviewed and analyzed.

Conclusion

The barriers identified were categorized into different themes including technology, data, regulation, human resources, education, and culture. Many of these challenges are ubiquitous across artificial intelligence implementation in any medical diagnostic setting. Future research directions include developing solutions to the barriers presented in this project.

Graphical abstract

Cardiopulmonary Coupling

Cardiopulmonary coupling (CPC) is a technique that generates sleep spectrogram by calculating the cross-spectral power and coherence of heart rate variability and respiratory tidal volume fluctuations. There are several forms of CPC in the sleep spectrogram, which may provide information about normal sleep physiology and pathological sleep states. Since CPC can be calculated from any signal recording containing heart rate and respiration information, such as photoplethysmography (PPG) or blood pressure, it can be widely used in various applications, including wearables and non-contact devices. When derived from PPG, an automatic apnea-hypopnea index can be calculated from CPC-oximetry as PPG can be obtained from oximetry alone. CPC-based sleep profiling reveals the effects of stable and unstable sleep on sleep apnea, insomnia, cardiovascular regulation, and metabolic disorders. Here, we introduce, with examples, the current knowledge and understanding of the CPC technique, especially the physiological basis, analytical methods, and its clinical applications.

Cardiopulmonary Sleep Spectrograms Open a Novel Window Into Sleep Biology-Implications for Health and Disease

The interactions of heart rate variability and respiratory rate and tidal volume fluctuations provide key information about normal and abnormal sleep. A set of metrics can be computed by analysis of coupling and coherence of these signals, cardiopulmonary coupling (CPC). There are several forms of CPC, which may provide information about normal sleep physiology, and pathological sleep states ranging from insomnia to sleep apnea and hypertension. As CPC may be computed from reduced or limited signals such as the electrocardiogram or photoplethysmogram (PPG) vs. full polysomnography, wide application including in wearable and non-contact devices is possible. When computed from PPG, which may be acquired from oximetry alone, an automated apnea hypopnea index derived from CPC-oximetry can be calculated. Sleep profiling using CPC demonstrates the impact of stable and unstable sleep on insomnia (exaggerated variability), hypertension (unstable sleep as risk factor), improved glucose handling (associated with stable sleep), drug effects (benzodiazepines increase sleep stability), sleep apnea phenotypes (obstructive vs. central sleep apnea), sleep fragmentations due to psychiatric disorders (increased unstable sleep in depression).