Assessment of TP53 and CDKN2A status as predictive markers of malignant transformation of sinonasal inverted papilloma.

The mechanism and predictive biomarkers of sinonasal inverted papilloma (IP) transformation into squamous cell carcinoma (SCC) are still unclear. We investigated the genetic mutations involved and the predictive biomarkers. Fourteen patients with SCC arising from IP and six patients with IPs without malignant transformation (sIP) were included. DNA was extracted separately from areas of normal tissue, IP, dysplasia, and SCC. Whole exome sequencing and immunohistochemistry was performed. Major oncogenic mutations were observed in the progression from IP to SCC. The most frequently mutated genes were TP53 (39%) and CDKN2A (27%). Mutations in TP53 and/or CDKN2A were observed in three of six IPs with malignant transformation (cIP); none were observed in sIPs. Tumor mutational burden (TMB) increased from IP to SCC (0.64/Mb, 1.11/Mb, and 1.25 for IP, dysplasia, and SCC, respectively). TMB was higher in the cIPs than in the sIPs (0.64/Mb vs 0.3/Mb). Three cIPs showed a diffuse strong or null pattern in p53, and one showed a total loss of p16, a distinct pattern from sIPs. Our result suggests that TP53 and CDKN2A status can be predictive markers of malignant transformation of IP. Furthermore, immunohistochemistry of p53 and p16 expression can be surrogate markers for TP53 and CDKN2A status.

Validation of Korean Olfactory Questionnaire for Perioperative Olfactory Assessment in Endoscopic Endonasal Skull Base Surgery.

Objective: To analyze changes in olfactory function after endoscopic endonasal skull base surgery and compare performance of the olfactory questionnaire with those of conventional psychophysical tests. Methods: Patients were classified into 5 categories for olfactory function evaluation (normal, mild hyposmia, moderate hyposmia, severe hyposmia, and anosmia) based on a self-assessment. Patients also underwent the butanol threshold test (BTT), Cross-Cultural Smell Identification Test (CCSIT), and 11-item olfactory questionnaire. Subjects with normosmia preoperatively and who were followed up at least 6 months after surgery were analyzed. Receiver operating characteristic curves and confusion matrix analysis were performed for BTT, CCSIT, and olfactory questionnaire to compare their diagnostic abilities. The effects of age, preoperative olfaction, septal flap, tumor pathology, and tumor size on postoperative olfaction were evaluated using multivariate linear regression analysis. Results: Data from 108 patients were analyzed. Postoperative changes in the olfactory questionnaire were significantly associated with changes in the BTT and CCSIT. The area under the curve for postoperative self-olfactory function classification was highest for olfactory questionnaire (0.894), followed by BTT (0.767) and CCSIT (0.688). Patient age at the time of surgery and preoperative BTT score were significantly related to postoperative olfactory outcomes. Conclusion: The olfactory questionnaire correlated well with conventional psychosomatic olfactory function tests. In combination with clinical parameters and preoperative psychosomatic olfactory function tests, the olfactory questionnaire is suitable for assessing subjective olfactory function after endoscopic endonasal skull base surgery.

Predicting Obstructive Sleep Apnea Based on Computed Tomography Scan Using Deep Learning Models.

RATIONALE: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population due to limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. OBJECTIVES: To predict OSA and its severity based on paranasal CT using a 3-dimensional deep learning algorithm. METHODS: One internal dataset (n=798) and two external datasets (n=135 and 85) were used in this study. In the internal dataset, 92 normal, 159 mild, 201 moderate, and 346 severe OSA participants were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a 3-dimensional convolutional neural network (CNN)-based part treating unstructured data (CT images) and a multi-layer perceptron (MLP)-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. MEASUREMENTS AND MAIN RESULTS: In four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI] 86.8-88.6) in the internal dataset and 84.0% (95% CI 83.0-85.1) and 86.3% (95% CI 85.3-87.3) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), The area under the receiver operating characteristics (AUROC), accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI 0.899-0.922), 91.0% (95% CI 90.1-91.9), 89.9% (95% CI 88.8-90.9), 93.5% (95% CI 92.7-94.3), and 93.2% (95% CI 92.5-93.9), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and AUROC for two-class classification (p<0.001). CONCLUSIONS: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis.

Treatment outcome and prognostic factors of inverted papilloma involving the frontal sinus.

Objectives: This study aimed to evaluate the characteristics and treatment outcomes of inverted papillomas involving the frontal sinus. Methods: Patients treated for inverted papilloma involving the frontal sinus between 2003 and 2020 were reviewed. Tumors were classified based on their extent (Extent 1: partially encroaching on the frontal sinus; Extent 2: completely filling the frontal sinus; Extent 3: eroding bony borders beyond the frontal sinus) and site of origin (Origin 1: originating outside the frontal sinus and prolapsing into the frontal sinus; Origin 2: originating from the frontal sinus walls medial to the vertical plane of the lamina papyracea; Origin 3: originating from the frontal sinus walls lateral to the vertical plane of the lamina papyracea). Treatment outcomes including tumor recurrence and patency of the frontal recess were analyzed according to tumor characteristics and surgical treatment modalities. Results: A total of 49 surgical cases were analyzed. Extent 1 were the most common type (n = 27), followed by Extent 2 (n = 15), and Extent 3 (n = 7). The most common sites of origin were Origin 1 (n = 23), followed by Origin 2 (n = 15), and Origin 3 (n = 11). Overall, there were nine recurrences (18.4%). Recurrence was not associated with tumor extent, whereas tumor origin, particularly Origin 3 was associated with higher recurrence; 1/23 (4.3%) for Origin 1, 3/15 (20.0%) for Origin 2, and 5/11 (45.5%) for Origin 3 (Log-rank p < .001). Draf III frontal sinusotomy was associated with in the highest patency rate (84.6%) during the follow-up. Conclusion: The recurrence rate of frontal sinus inverted papilloma depends on tumor origin rather than the extent of the tumor. In particular, lesions originating from the frontal sinus lateral to the lamina papyracea recur frequently. Draf III frontal sinusotomy can achieve patent frontal recess allowing active surveillance. Level of Evidence: IV.

How to objectively measure snoring: a systematic review.

PURPOSE: Snoring is the most common symptom of obstructive sleep apnea. Various objective methods of measuring snoring are available, and even if the measurement is performed the same way, communication is difficult because there are no common reference values between the researcher and clinician with regard to intensity and frequency, among other variables. In other words, no consensus regarding objective measurement has been reached. This study aimed to review the literature related to the objective measurement of snoring, such as measurement devices, definitions, and device locations. METHODS: A literature search based on the PubMed, Cochrane, and Embase databases was conducted from the date of inception to April 5, 2023. Twenty-nine articles were included in this study. Articles that mentioned only the equipment used for measurement and did not include individual details were excluded from the study. RESULTS: Three representative methods for measuring snoring emerged. These include (1) a microphone, which measures snoring sound; (2) piezoelectric sensor, which measures snoring vibration; and (3) nasal transducer, which measures airflow. In addition, recent attempts have been made to measure snoring using smartphones and applications. CONCLUSION: Numerous studies have investigated both obstructive sleep apnea and snoring. However, the objective methods of measuring snoring and snoring-related concepts vary across studies. Consensus in the academic and clinical communities on how to measure and define snoring is required.

In-Home Smartphone-Based Prediction of Obstructive Sleep Apnea in Conjunction With Level 2 Home Polysomnography.

Importance: Consumer-level sleep analysis technologies have the potential to revolutionize the screening for obstructive sleep apnea (OSA). However, assessment of OSA prediction models based on in-home recording data is usually performed concurrently with level 1 in-laboratory polysomnography (PSG). Establishing the predictability of OSA using sound data recorded from smartphones based on level 2 PSG at home is important. Objective: To validate the performance of a prediction model for OSA using breathing sound recorded from smartphones in conjunction with level 2 PSG at home. Design, Setting, and Participants: This diagnostic study followed a prospective design, involving participants who underwent unattended level 2 home PSG. Breathing sounds were recorded during sleep using 2 smartphones, one with an iOS operating system and the other with an Android operating system, simultaneously with home PSG in participants' own home environment. Participants were 19 years and older, slept alone, and had either been diagnosed with OSA or had no previous diagnosis. The study was performed between February 2022 and February 2023. Main Outcomes and Measures: Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the predictive model based on the recorded breathing sounds. Results: Of the 101 participants included during the study duration, the mean (SD) age was 48.3 (14.9) years, and 51 (50.5%) were female. For the iOS smartphone, the sensitivity values at apnea-hypopnea index (AHI) levels of 5, 15, and 30 per hour were 92.6%, 90.9%, and 93.3%, respectively, with specificities of 84.3%, 94.4%, and 94.4%, respectively. Similarly, for the Android smartphone, the sensitivity values at AHI levels of 5, 15, and 30 per hour were 92.2%, 90.0%, and 92.9%, respectively, with specificities of 84.0%, 94.4%, and 94.3%, respectively. The accuracy for the iOS smartphone was 88.6%, 93.3%, and 94.3%, respectively, and for the Android smartphone was 88.1%, 93.1%, and 94.1% at AHI levels of 5, 15, and 30 per hour, respectively. Conclusions and Relevance: This diagnostic study demonstrated the feasibility of predicting OSA with a reasonable level of accuracy using breathing sounds obtained by smartphones during sleep at home.

The 24-Hour Cardiac Autonomic Activity in Patients With Allergic Rhinitis.

BACKGROUND: Definitive knowledge of the 24-hour cardiac autonomic activity in patients with allergic rhinitis (AR) is lacking. Thus, we aimed to evaluate heart rate variability (HRV), which is used to measure cardiac autonomic activity by 24-hour Holter monitoring in patients with AR. METHODS: We enrolled 32 patients who visited our clinic and were diagnosed with AR. The control group was selected four-fold (n = 128) by matching (age, sex, hypertension, and diabetes) in the AR group from a Holter registry in the cardiology department. The HRV results, which were measured using 24-hour Holter monitoring, were compared between the AR and control groups. RESULTS: All time-domain parameters of HRV revealed no differences between the groups. However, among the frequency domain parameters of HRV, the low-frequency to high-frequency ratio and low-frequency power in normalized units were significantly lower in the AR group. Conversely, high-frequency power in normalized units was significantly higher in the AR group. In the multiple regression analysis, AR was independently associated with sympathetic withdrawal (adjusted odds ratio = 3.393, P = 0.020) after adjusting for age, sex, hypertension, diabetes mellitus, and hyperlipidemia. CONCLUSIONS: The present findings suggest differences in cardiac autonomic activity which are related with sympathetic withdrawal in patients with AR compared with that in the normal population over 24 hours.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study.

BACKGROUND: Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography. OBJECTIVE: This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment. METHODS: The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography. RESULTS: The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal. CONCLUSIONS: Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Snoring-related polygenic risk and its relationship with lifestyle factors in a Korean population: KoGES study.

Whereas lifestyle-related factors are recognized as snoring risk factors, the role of genetics in snoring remains uncertain. One way to measure the impact of genetic risk is through the use of a polygenic risk score (PRS). In this study, we aimed to investigate whether genetics plays a role in snoring after adjusting for lifestyle factors. Since the effect of polygenic risks may differ across ethnic groups, we calculated the PRS for snoring from the UK Biobank and applied it to a Korean cohort. We sought to evaluate the reproducibility of the UK Biobank PRS for snoring in the Korean cohort and to investigate the interaction of lifestyle factors and genetic risk on snoring in the Korean population. In this study, we utilized a Korean cohort obtained from the Korean Genome Epidemiology Study (KoGES). We computed the snoring PRS for the Korean cohort based on the UK Biobank PRS. We investigated the relationship between polygenic risks and snoring while controlling for lifestyle factors, including sex, age, body mass index (BMI), alcohol consumption, smoking, physical activity, and sleep time. Additionally, we analyzed the interaction of each lifestyle factor and the genetic odds of snoring. We included 3526 snorers and 1939 nonsnorers from the KoGES cohort and found that the PRS, a polygenic risk factor, was an independent factor for snoring after adjusting for lifestyle factors. In addition, among lifestyle factors, higher BMI, male sex, and older age were the strongest lifestyle factors for snoring. In addition, the highest adjusted odds ratio for snoring was higher BMI (OR 1.98, 95% CI 1.76-2.23), followed by male sex (OR 1.54, 95% CI 1.28-1.86), older age (OR 1.23, 95% CI 1.03-1.35), polygenic risks such as higher PRS (OR 1.18, 95% CI 1.08-1.29), drinking behavior (OR 1.18, 95% CI 1.03-1.35), late sleep mid-time (OR 1.17, 95% CI 1.02-1.33), smoking behavior (OR 0.99, 95% CI 0.82-1.19), and lower physical activity (OR 0.92, 95% CI 0.85-1.00). Our study identified that the UK Biobank PRS for snoring was reproducible in the Korean cohort and that genetic risk served as an independent risk factor for snoring in the Korean population. These findings may help to develop personalized approaches to reduce snoring in individuals with high genetic risk.

Differences in Clinical and Immunological Characteristics According to the Various Criteria for Tissue Eosinophilia in Chronic Rhinosinusitis With Nasal Polyps.

OBJECTIVES: Several criteria exist for classifying chronic rhinosinusitis with nasal polyps (CRSwNP) as eosinophilic or non-eosinophilic. This study attempted to evaluate several criteria for defining eosinophilic CRSwNP from clinical and immunological perspectives. METHODS: A cohort of 84 patients (73 patients with CRSwNP and 11 control patients) was retrospectively analyzed. Patients were divided into eosinophilic and non-eosinophilic CRSwNP based on four different criteria: eosinophils (EOS) accounting for more than 20% of the total inflammatory cells; ≥70 EOS per high-power field (HPF); >55 EOS/HPF; and ≥10 EOS/HPF. Preoperative clinical characteristics, the immunological profiles of 14 cytokines from nasal tissue, and postoperative outcomes were compared between eosinophilic and non-eosinophilic CRSwNP based on each criterion. These criteria were immunologically validated by using 14 cytokines to predict the performance of tissue eosinophilia with a random forest model. RESULTS: Patients with eosinophilic CRSwNP were significantly older when the criterion of ≥10 EOS/HPF or EOS >20% was used. The number of patients with aspirin intolerance was significantly higher in eosinophilic CRSwNP based on the criterion of EOS >20%. From an immunological perspective, non-type 2 inflammatory cytokines were significantly higher in non-eosinophilic CRSwNP with the criterion of EOS >20% of the total inflammatory cells. In addition, the criterion of EOS >20% of the total inflammatory cells resulted in the best prediction of eosinophilic CRSwNP, with an accuracy of 88.10% and area under the curve of 0.94. CONCLUSION: Clinical and immunological characteristics were different between eosinophilic and non-eosinophilic CRSwNP depending on a variety of criteria, and the. RESULTS: of this study should be taken into account when choosing the criterion for defining eosinophilic CRSwNP and interpreting the data accordingly.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.

Prediction of Sleep Stages Via Deep Learning Using Smartphone Audio Recordings in Home Environments: Model Development and Validation.

BACKGROUND: The growing public interest and awareness regarding the significance of sleep is driving the demand for sleep monitoring at home. In addition to various commercially available wearable and nearable devices, sound-based sleep staging via deep learning is emerging as a decent alternative for their convenience and potential accuracy. However, sound-based sleep staging has only been studied using in-laboratory sound data. In real-world sleep environments (homes), there is abundant background noise, in contrast to quiet, controlled environments such as laboratories. The use of sound-based sleep staging at homes has not been investigated while it is essential for practical use on a daily basis. Challenges are the lack of and the expected huge expense of acquiring a sufficient size of home data annotated with sleep stages to train a large-scale neural network. OBJECTIVE: This study aims to develop and validate a deep learning method to perform sound-based sleep staging using audio recordings achieved from various uncontrolled home environments. METHODS: To overcome the limitation of lacking home data with known sleep stages, we adopted advanced training techniques and combined home data with hospital data. The training of the model consisted of 3 components: (1) the original supervised learning using 812 pairs of hospital polysomnography (PSG) and audio recordings, and the 2 newly adopted components; (2) transfer learning from hospital to home sounds by adding 829 smartphone audio recordings at home; and (3) consistency training using augmented hospital sound data. Augmented data were created by adding 8255 home noise data to hospital audio recordings. Besides, an independent test set was built by collecting 45 pairs of overnight PSG and smartphone audio recording at homes to examine the performance of the trained model. RESULTS: The accuracy of the model was 76.2% (63.4% for wake, 64.9% for rapid-eye movement [REM], and 83.6% for non-REM) for our test set. The macro F1-score and mean per-class sensitivity were 0.714 and 0.706, respectively. The performance was robust across demographic groups such as age, gender, BMI, or sleep apnea severity (accuracy 73.4%-79.4%). In the ablation study, we evaluated the contribution of each component. While the supervised learning alone achieved accuracy of 69.2% on home sound data, adding consistency training to the supervised learning helped increase the accuracy to a larger degree (+4.3%) than adding transfer learning (+0.1%). The best performance was shown when both transfer learning and consistency training were adopted (+7.0%). CONCLUSIONS: This study shows that sound-based sleep staging is feasible for home use. By adopting 2 advanced techniques (transfer learning and consistency training) the deep learning model robustly predicts sleep stages using sounds recorded at various uncontrolled home environments, without using any special equipment but smartphones only.

Associations Between Systemic Inflammatory Markers Based on Blood Cells and Polysomnographic Factors in Obstructive Sleep Apnea.

OBJECTIVES: Systemic inflammation plays a key role in the pathogenesis of obstructive sleep apnea (OSA); however, easy-to-use methods to evaluate the severity of systemic inflammation have yet to be developed. This study investigated the association between systemic inflammation markers that could be derived from the complete blood count (CBC) profile and sleep parameters in a large number of patients with OSA. METHODS: Patients who visited our hospital's Otorhinolaryngology Sleep Clinic between January 2017 and April 2022 underwent polysomnography and routine laboratory tests, including a CBC. Associations between three systemic inflammatory markers-the systemic immune-inflammation index (SII), neutrophil-lymphocyte ratio (NLR), and platelet-lymphocyte ratio (PLR)-and polysomnographic and demographic factors including age, sex, body mass index, the apnea-hypopnea index (AHI), the hypopnea index (HI), lowest oxygen saturation (%), the Pittsburgh Sleep Quality Index (PSQI), the Epworth Sleepiness Scale, and percentages of non-rapid eye movement (REM) sleep stage 3, REM sleep, and snoring time were analyzed. The inflammation markers were compared among OSA subgroups, and associations were also analyzed in subgroups with different OSA severities. RESULTS: In total, 1,102 patients (968 men and 134 women) were included, and their mean AHI was 33.0±24.3. PSQI was significantly associated with SII (P=0.027). No independent significant factors were identified for the NLR or PLR. Within the simple snoring and mild OSA subgroups, no significant association was found between sleep parameters and the SII. In the severe OSA subgroup, the AHI (P=0.004) and PSQI (P=0.012) were independently associated with the SII. CONCLUSION: Our study analyzed systemic inflammatory markers based on the CBC, a simple, relatively cost-effective test, and showed that the AHI and SII were significantly correlated only in the severe OSA subgroup.

Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises: Algorithm Development and Validation.

BACKGROUND: Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home. OBJECTIVE: The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist. METHODS: This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on breathing sounds that occur during sleep. The whole breathing sound of each night was divided into 30-second epochs and labeled as "apnea," "hypopnea," or "no-event," and the home noises were used to make the model robust to a noisy home environment. The performance of the prediction model was assessed using epoch-by-epoch prediction accuracy and OSA severity classification based on the apnea-hypopnea index (AHI). RESULTS: Epoch-by-epoch OSA event detection showed an accuracy of 86% and a macro F1-score of 0.75 for the 3-class OSA event detection task. The model had an accuracy of 92% for "no-event," 84% for "apnea," and 51% for "hypopnea." Most misclassifications were made for "hypopnea," with 15% and 34% of "hypopnea" being wrongly predicted as "apnea" and "no-event," respectively. The sensitivity and specificity of the OSA severity classification (AHI≥15) were 0.85 and 0.84, respectively. CONCLUSIONS: Our study presents a real-time epoch-by-epoch OSA detector that works in a variety of noisy home environments. Based on this, additional research is needed to verify the usefulness of various multinight monitoring and real-time diagnostic technologies in the home environment.

A Classifying Model of Obstructive Sleep Apnea Based on Heart Rate Variability in a Large Korean Population.

BACKGROUND: The majority of patients with obstructive sleep apnea do not receive timely diagnosis and treatment because of the complexity of a diagnostic test. We aimed to predict obstructive sleep apnea based on heart rate variability, body mass index, and demographic characteristics in a large Korean population. METHODS: Models of binary classification for predicting obstructive sleep apnea severity were constructed using 14 features including 11 heart rate variability variables, age, sex, and body mass index. Binary classification was conducted separately using apnea-hypopnea index thresholds of 5, 15, and 30. Sixty percent of the participants were randomly allocated to training and validation sets while the other forty percent were designated as the test set. Classifying models were developed and validated with 10-fold cross-validation using logistic regression, random forest, support vector machine, and multilayer perceptron algorithms. RESULTS: A total of 792 (651 men and 141 women) subjects were included. The mean age, body mass index, and apnea-hypopnea index score were 55.1 years, 25.9 kg/m², and 22.9, respectively. The sensitivity of the best performing algorithm was 73.6%, 70.7%, and 78.4% when the apnea-hypopnea index threshold criterion was 5, 10, and 15, respectively. The prediction performances of the best classifiers at apnea-hypopnea indices of 5, 15, and 30 were as follows: accuracy, 72.2%, 70.0%, and 70.3%; specificity, 64.6%, 69.2%, and 67.9%; area under the receiver operating characteristic curve, 77.2%, 73.5%, and 80.1%, respectively. Overall, the logistic regression model using the apnea-hypopnea index criterion of 30 showed the best classifying performance among all models. CONCLUSION: Obstructive sleep apnea was fairly predicted by using heart rate variability, body mass index, and demographic characteristics in a large Korean population. Prescreening and continuous treatment monitoring of obstructive sleep apnea may be possible simply by measuring heart rate variability.

Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.

Thus far, there have been no reported specific rules for systematically determining the appropriate augmented sample size to optimize model performance when conducting data augmentation. In this paper, we report on the feasibility of synthetic data augmentation using generative adversarial networks (GAN) by proposing an automation pipeline to find the optimal multiple of data augmentation to achieve the best deep learning-based diagnostic performance in a limited dataset. We used Waters' view radiographs for patients diagnosed with chronic sinusitis to demonstrate the method developed herein. We demonstrate that our approach produces significantly better diagnostic performance parameters than models trained using conventional data augmentation. The deep learning method proposed in this study could be implemented to assist radiologists in improving their diagnosis. Researchers and industry workers could overcome the lack of training data by employing our proposed automation pipeline approach in GAN-based synthetic data augmentation. This is anticipated to provide new means to overcome the shortage of graphic data for algorithm training.

End-to-End Sleep Staging Using Nocturnal Sounds from Microphone Chips for Mobile Devices.

Purpose: Nocturnal sounds contain numerous information and are easily obtainable by a non-contact manner. Sleep staging using nocturnal sounds recorded from common mobile devices may allow daily at-home sleep tracking. The objective of this study is to introduce an end-to-end (sound-to-sleep stages) deep learning model for sound-based sleep staging designed to work with audio from microphone chips, which are essential in mobile devices such as modern smartphones. Patients and Methods: Two different audio datasets were used: audio data routinely recorded by a solitary microphone chip during polysomnography (PSG dataset, N=1154) and audio data recorded by a smartphone (smartphone dataset, N=327). The audio was converted into Mel spectrogram to detect latent temporal frequency patterns of breathing and body movement from ambient noise. The proposed neural network model learns to first extract features from each 30-second epoch and then analyze inter-epoch relationships of extracted features to finally classify the epochs into sleep stages. Results: Our model achieved 70% epoch-by-epoch agreement for 4-class (wake, light, deep, REM) sleep stage classification and robust performance across various signal-to-noise conditions. The model performance was not considerably affected by sleep apnea or periodic limb movement. External validation with smartphone dataset also showed 68% epoch-by-epoch agreement. Conclusion: The proposed end-to-end deep learning model shows potential of low-quality sounds recorded from microphone chips to be utilized for sleep staging. Future study using nocturnal sounds recorded from mobile devices at home environment may further confirm the use of mobile device recording as an at-home sleep tracker.

Aggressive Treatment Including Endonasal Surgical Sequestrectomy with Vascularized Nasoseptal Flap Can Improve Outcomes of Skull Base Osteoradionecrosis.

Objective Skull base osteoradionecrosis (SB-ORN) is a serious, potentially lethal complication of radiation therapy. We aimed to review the clinical characteristics and outcomes of SB-ORN according to the extent of treatment. Design Retrospective analysis design was used for this study. Setting The study was conducted in two tertiary care hospitals. Participants Patients included who had been clinically diagnosed with SB-ORN from January 2006 to 2017. Main Outcome Measures Clinical characteristics, including demographics, predisposing factors, presenting symptoms, radiological findings, treatment modalities, and treatment outcomes, were reviewed. Treatment was classified into conservative and aggressive types. Aggressive treatment included radical surgical removal of soft tissue and bony sequestrum with the placement of vascularized tissue. Treatment outcome was analyzed in terms of clinical control, survival, and carotid artery blow out. Results Fifteen patients (11 males and 4 females) were identified during the study period. Eight patients were managed conservatively, whereas seven patients were managed with aggressive treatment. The 2-year survival was 75% in the aggressive treatment group and 15% in the conservative group (log-rank, p = 0.049). The estimated 2-year blow out free rate was 46.7% for the conservative group and 100% for the aggressive group (log-rank, p = 0.100). Conclusion In patients with SB-ORN, aggressive management, including surgical removal of sequestrum and coverage with a pedicled flap, is associated with increased survival.