Cost-utility analysis of palivizumab for preventing respiratory syncytial virus in preterm neonates and infants in Colombia.

AIM: Palivizumab has proven effective in reducing hospitalizations, preventing severe illness, improving health outcomes, and reducing healthcare costs for infants at risk of respiratory syncytial virus (RSV) infection. We aim to assess the value of palivizumab in preventing RSV infection in high-risk infants in Colombia, where RSV poses a significant threat, causing severe respiratory illness and hospitalizations. METHODS: We conducted a decision tree analysis to compare five doses of palivizumab with no palivizumab. The study considered three population groups: preterm neonates (≤ 35 weeks gestational age), infants with bronchopulmonary dysplasia (BPD), and infants with hemodynamically significant congenital heart disease (CHD). We obtained clinical efficacy data from IMpact-RSV and Cardiac Synagis trials, while we derived neonatal hospitalization risks from the SENTINEL-1 study. We based hospitalization and recurrent wheezing management costs on Colombian analyses and validated them by experts. We estimated incremental cost-effectiveness ratios and performed 1,000 Monte Carlo simulations for probabilistic sensitivity analyses. RESULTS: Palivizumab is a dominant strategy for preventing RSV infection in preterm neonates and infants with BPD and CHD. Its high efficacy (78% in preventing RSV in preterm infants), the substantial risk of illness and hospitalization, and the high costs associated with hospitalization, particularly in neonatal intensive care settings, support this finding. The scatter plots and willingness-to-pay curves align with these results. CONCLUSION: Palivizumab is a cost-saving strategy in Colombia, effectively preventing RSV infection in preterm neonates and infants with BPD and CHD by reducing hospitalizations and lowering healthcare costs.

Feasibility and Acceptability of Pediatric Smartphone Lung Auscultation by Parents: Cross-Sectional Study.

Background: The use of a smartphone built-in microphone for auscultation is a feasible alternative to the use of a stethoscope, when applied by physicians. Objective: This cross-sectional study aims to assess the feasibility of this technology when used by parents-the real intended end users. Methods: Physicians recruited 46 children (male: n=33, 72%; age: mean 11.3, SD 3.1 y; children with asthma: n=24, 52%) during medical visits in a pediatric department of a tertiary hospital. Smartphone auscultation using an app was performed at 4 locations (trachea, right anterior chest, and right and left lung bases), first by a physician (recordings: n=297) and later by a parent (recordings: n=344). All recordings (N=641) were classified by 3 annotators for quality and the presence of adventitious sounds. Parents completed a questionnaire to provide feedback on the app, using a Likert scale ranging from 1 ("totally disagree") to 5 ("totally agree"). Results: Most recordings had quality (physicians' recordings: 253/297, 85.2%; parents' recordings: 266/346, 76.9%). The proportions of physicians' recordings (34/253, 13.4%) and parents' recordings (31/266, 11.7%) with adventitious sounds were similar. Parents found the app easy to use (questionnaire: median 5, IQR 5-5) and were willing to use it (questionnaire: median 5, IQR 5-5). Conclusions: Our results show that smartphone auscultation is feasible when performed by parents in the clinical context, but further investigation is needed to test its feasibility in real life.

A novel method in COPD diagnosing using respiratory signal generation based on CycleGAN and machine learning.

OBJECTIVE: The main goal of this research is to use distinctive features in respiratory sounds for diagnosing Chronic Obstructive Pulmonary Disease (COPD). This study develops a classification method by utilizing inverse transforms to effectively identify COPD based on unique respiratory features while comparing the classification performance of various optimal algorithms. METHOD: Respiratory sounds are divided into individual breathing cycles. In the data standardization and augmentation phase, the CycleGAN model enhances data diversity. Comprehensive analyses for these segments are then implemented using various Wavelet families and different spectral transformations representing characteristic signals. Advanced convolutional neural networks, including VGG16, ResNet50, and InceptionV3, are used for the classification task. RESULTS: The results of this study demonstrate the effectiveness of the mentioned method. Notably, the best-performing method utilizes Wavelet Bior1.3 after standardization in combination with InceptionV3, achieving a remarkable 99.75% F1-score, the gold standard for classification accuracy. CONCLUSION: Inverse transformation techniques combined with deep learning models show significant accuracy in detecting COPD disease. These findings suggest the feasibility of early COPD diagnosis through AI-powered characterization of acoustic features. MOTIVATION AND SIGNIFICANCE: The motivation behind this research stems from the urgent need for early and accurate diagnosis of Chronic Obstructive Pulmonary Disease (COPD). COPD is a respiratory disease that poses many difficulties when detected late, potentially causing severe harm to the patient's quality of life and increasing the healthcare burden. Timely identification and intervention are crucial to reduce the progression of the disease and improve patient outcomes.

Lung disease recognition methods using audio-based analysis with machine learning.

The use of computer-based automated approaches and improvements in lung sound recording techniques have made lung sound-based diagnostics even better and devoid of subjectivity errors. Using a computer to evaluate lung sound features more thoroughly with the use of analyzing changes in lung sound behavior, recording measurements, suppressing the presence of noise contaminations, and graphical representations are all made possible by computer-based lung sound analysis. This paper starts with a discussion of the need for this research area, providing an overview of the field and the motivations behind it. Following that, it details the survey methodology used in this work. It presents a discussion on the elements of sound-based lung disease classification using machine learning algorithms. This includes commonly prior considered datasets, feature extraction techniques, pre-processing methods, artifact removal methods, lung-heart sound separation, deep learning algorithms, and wavelet transform of lung audio signals. The study introduces studies that review lung screening including a summary table of these references and discusses the literature gaps in the existing studies. It is concluded that the use of sound-based machine learning in the classification of respiratory diseases has promising results. While we believe this material will prove valuable to physicians and researchers exploring sound-signal-based machine learning, large-scale investigations remain essential to solidify the findings and foster wider adoption within the medical community.

Deep learning-based lung sound analysis for intelligent stethoscope.

Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .

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.

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.

BACKGROUND: Interstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence-assisted lung auscultation and ultrasound (LUS) could constitute an alternative to conventional, subjective, operator-related methods for the accurate and earlier diagnosis of these diseases. This protocol describes the standardised collection of digitally-acquired lung sounds and LUS images of adult outpatients with IPF, NSIP or COPD and a deep learning diagnostic and severity-stratification approach. METHODS: A total of 120 consecutive patients (≥ 18 years) meeting international criteria for IPF, NSIP or COPD and 40 age-matched controls will be recruited in a Swiss pulmonology outpatient clinic, starting from August 2022. At inclusion, demographic and clinical data will be collected. Lung auscultation will be recorded with a digital stethoscope at 10 thoracic sites in each patient and LUS images using a standard point-of-care device will be acquired at the same sites. A deep learning algorithm (DeepBreath) using convolutional neural networks, long short-term memory models, and transformer architectures will be trained on these audio recordings and LUS images to derive an automated diagnostic tool. The primary outcome is the diagnosis of ILD versus control subjects or COPD. Secondary outcomes are the clinical, functional and radiological characteristics of IPF, NSIP and COPD diagnosis. Quality of life will be measured with dedicated questionnaires. Based on previous work to distinguish normal and pathological lung sounds, we estimate to achieve convergence with an area under the receiver operating characteristic curve of > 80% using 40 patients in each category, yielding a sample size calculation of 80 ILD (40 IPF, 40 NSIP), 40 COPD, and 40 controls. DISCUSSION: This approach has a broad potential to better guide care management by exploring the synergistic value of several point-of-care-tests for the automated detection and differential diagnosis of ILD and COPD and to estimate severity. Trial registration Registration: August 8, 2022. CLINICALTRIALS: gov Identifier: NCT05318599.

Is sibship composition a risk factor for childhood asthma? Systematic review and meta-analysis.

BACKGROUND: Following the "hygiene hypothesis", the role of sibship composition in asthma and wheezing has been extensively studied, but the findings are inconsistent. For the first time, this systematic review and meta-analysis synthesized evidences from studies investigating the association of sibship size and birth order with risk of asthma and wheezing. METHODS: Fifteen databases were searched to identify eligible studies. Study selection and data extraction were performed independently by pairs of reviewers. Meta-analysis with robust variance estimation (RVE) was used to produce pooled risk ratio (RR) effect estimates from comparable numerical data. RESULTS: From 17,466 identified records, 158 reports of 134 studies (> 3 million subjects) were included. Any wheezing in the last ≤ 1.5 years occurred more frequently in infants with ≥ 1 sibling [pooled RR 1.10, 95% confidence interval (CI) 1.02-1.19] and ≥ 1 older sibling (pooled RR 1.16, 95% CI 1.04-1.29). The pooled effect sizes for asthma were overall statistically nonsignificant, although having ≥ 1 older sibling was marginally protective for subjects aged ≥ 6 years (pooled RR 0.93, 95% CI 0.88-0.99). The effect estimates weakened in studies published after 2000 compared with earlier studies. CONCLUSIONS: Being second-born or later and having at least one sibling is associated with a slightly increased risk of temporary wheezing in infancy. In contrast, being second-born or later is associated with marginal protection against asthma. These associations appear to have weakened since the turn of the millennium, possibly due to lifestyle changes and socioeconomic development. Video Abstract.

Coronavirus diagnosis using cough sounds: Artificial intelligence approaches.

Introduction: The Coronavirus disease 2019 (COVID-19) pandemic has caused irreparable damage to the world. In order to prevent the spread of pathogenicity, it is necessary to identify infected people for quarantine and treatment. The use of artificial intelligence and data mining approaches can lead to prevention and reduction of treatment costs. The purpose of this study is to create data mining models in order to diagnose people with the disease of COVID-19 through the sound of coughing. Method: In this research, Supervised Learning classification algorithms have been used, which include Support Vector Machine (SVM), random forest, and Artificial Neural Networks, that based on the standard "Fully Connected" neural network, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) recurrent neural networks have been established. The data used in this research was from the online site sorfeh.com/sendcough/en, which has data collected during the spread of COVID-19. Result: With the data we have collected (about 40,000 people) in different networks, we have reached acceptable accuracies. Conclusion: These findings show the reliability of this method for using and developing a tool as a screening and early diagnosis of people with COVID-19. This method can also be used with simple artificial intelligence networks so that acceptable results can be expected. Based on the findings, the average accuracy was 83% and the best model was 95%.

Smart Devices Are Poised to Revolutionize the Usefulness of Respiratory Sounds.

The association between breathing sounds and respiratory health or disease has been exceptionally useful in the practice of medicine since the advent of the stethoscope. Remote patient monitoring technology and artificial intelligence offer the potential to develop practical means of assessing respiratory function or dysfunction through continuous assessment of breathing sounds when patients are at home, at work, or even asleep. Automated reports such as cough counts or the percentage of the breathing cycles containing wheezes can be delivered to a practitioner via secure electronic means or returned to the clinical office at the first opportunity. This has not previously been possible. The four respiratory sounds that most lend themselves to this technology are wheezes, to detect breakthrough asthma at night and even occupational asthma when a patient is at work; snoring as an indicator of OSA or adequacy of CPAP settings; cough in which long-term recording can objectively assess treatment adequacy; and crackles, which, although subtle and often overlooked, can contain important clinical information when appearing in a home recording. In recent years, a flurry of publications in the engineering literature described construction, usage, and testing outcomes of such devices. Little of this has appeared in the medical literature. The potential value of this technology for pulmonary medicine is compelling. We expect that these tiny, smart devices soon will allow us to address clinical questions that occur away from the clinic.

Physicians' ability to recognize adventitious lung sounds.

BACKGROUND: Lung auscultation is an important tool for diagnosing respiratory diseases. However, the ability of observers to recognize respiratory sounds varies considerably and depends on the sound. The present study aimed to assess the auscultatory skills of healthcare professionals and medical students. METHODS: A total of 295 physicians (185 pediatricians, 69 pulmonologists, and 41 physicians of general/internal medicine and subspecialties), 55 residents, and 50 medical students participated in the survey. They listened to five audio-recorded respiratory sounds and described them in free-form answers. RESULTS: The rates of correct answers were 55.2% for fine crackles, 74.5% for coarse crackles, 72.2% for wheezes, 18.75% for squawks, and 11.25% for pleural friction rub. The medical specialty was correlated with the correct answers and both pediatricians and physicians of general/internal medicine and subspecialties recognized fewer sounds compared with respiratory physicians (odds ratio [OR]: 0.37; confidence interval [CI]: 0.22-0.62; p < 0.001 and, OR: 0.47; CI: 0.22-0.99, p = 0.048, respectively). Years of experience were negatively correlated with the number of correct answers (OR: 0.73; CI:0.62-0.84; p = 0.001). CONCLUSIONS: Gaps remain in both terminology and recognition of lung sounds among a wide population of Greek physicians. Less experienced physicians perform better on lung auscultation, indicating that continuing education with critical feedback should be offered.

Actualización del consenso chileno SOCHINEP-SER para el manejo de las sibilancias recurrentes del preescolar / Update of the chilean consensus SOCHINEP-SER for the management of recurrent wheezing in preschool children

Las sibilancias recurrentes del preescolar son un problema prevalente. 50% de todos los niños tiene al menos un episodio de sibilancias en los primeros 6 años. Sin embargo, solo 4 % de los menores de 4 años tiene diagnóstico de asma. Por este motivo es fundamental realizar una adecuada anamnesis y examen físico tendientes a descartar causas secundarias, lo que debe ser complementado con exámenes de laboratorio de acuerdo con la orientación clínica. En la actualidad se recomienda indicar tratamiento de mantención con corticoides inhalados en aquellos niños que tengan episodios repetidos de obstrucción bronquial y que tengan una alta probabilidad de respuesta favorable a esta terapia. Se ha demostrado que aquellos pacientes que tienen un recuento de eosinófilos en sangre > 300 células por mm3 o aquellos que presentan una prueba cutánea positiva o IgE específicas positivas para alérgenos inhalados, responderán adecuadamente al tratamiento con esteroides inhalados.

Recurrent wheezing in preschoolers has a high prevalence. 50% of all children have at least one wheezing episode in the first six years of life. However, only 4% of children under four years of age are diagnosed with asthma. Therefore, it is essential to carry out an adequate medical history and physical examination to rule out secondary causes, which must be complemented with laboratory tests in accordance with clinical guidance. It is recommended to indicate maintenance treatment with inhaled corticosteroids to those children who have repeated episodes of wheezing and who have a high probability of a good response to this therapy. It has been demonstrated that those patients who have blood eosinophil count > 300 cells per mm3 or those who have a positive skin test or positive specific IgE for inhaled allergens will have a good response to inhaled corticosteroids.

Actualización del consenso chileno SOCHINEP-SER para el manejo de las sibilancias recurrentes del preescolar / Update of the chilean consensus SOCHINEP-SER for the management of recurrent wheezing in preschool children

Las sibilancias recurrentes del preescolar son un problema prevalente. 50% de todos los niños tiene al menos un episodio de sibilancias en los primeros 6 años. Sin embargo, solo 4% de los menores de 4 años tiene diagnóstico de asma. Por este motivo es fundamental realizar una adecuada anamnesis y examen físico tendientes a descartar causas secundarias, lo que debe ser complementado con exámenes de laboratorio de acuerdo con la orientación clínica. En la actualidad se recomienda indicar tratamiento de mantención con corticoides inhalados en aquellos niños que tengan episodios repetidos de obstrucción bronquial y que tengan una alta probabilidad de respuesta favorable a esta terapia. Se ha demostrado que aquellos pacientes que tienen un recuento de eosinófilos en sangre > 300 células por mm3 o aquellos que presentan una prueba cutánea positiva o IgE específicas positivas para alergenos inhalados responderán adecuadamente al tratamiento con esteroides inhalados.

Recurrent wheezing in preschoolers has a high prevalence. 50% of all children have at least one wheezing episode in the first six years of life. However, only 4% of children under four years of age are diagnosed with asthma. Therefore it is essential to carry out an adequate medical history and physical examination to rule out secondary causes, which must be complemented with laboratory tests in accordance with clinical guidance. It is recommended to indicate maintenance treatment with inhaled corticosteroids to those children who have repeated episodes of wheezing and who have a high probability of a good response to this therapy. It has been demonstrated that those patients who have blood eosinophil count > 300 cells per mm3 or those who have a positive skin test or positive specific IgE for inhaled allergens will have a good response to inhaled corticosteroids.

Reliability and Validity of Computerized Adventitious Respiratory Sounds in People with Bronchiectasis.

Background: Computerized adventitious respiratory sounds (ARS), such as crackles and wheezes, have been poorly explored in bronchiectasis, especially their measurement properties. This study aimed to test the reliability and validity of ARS in bronchiectasis. Methods: Respiratory sounds were recorded twice at 4 chest locations on 2 assessment sessions (7 days apart) in people with bronchiectasis and daily sputum expectoration. The total number of crackles, number of wheezes and wheeze occupation rate (%) were the parameters extracted. Results: 28 participants (9 men; 62 ± 12 y) were included. Total number of crackles and wheezes showed moderate within-day (ICC 0.87, 95% CI 0.74−0.94; ICC 0.86, 95% CI 0.71−0.93) and between-day reliability (ICC 0.70, 95% CI 0.43−0.86; ICC 0.78, 95% CI 0.56−0.90) considering all chest locations and both respiratory phases; wheeze occupation rate showed moderate within-day reliability (ICC 0.86, 95% CI 0.71−0.93), but poor between-day reliability (ICC 0.71, 95% CI 0.33−0.87). Bland−Altman plots revealed no systematic bias, but wide limits of agreement, particularly in the between-days analysis. All ARS parameters correlated moderately with the amount of daily sputum expectoration (r > 0.4; p < 0.05). No other significant correlations were observed. Conclusion: ARS presented moderate reliability and were correlated with the daily sputum expectoration in bronchiectasis. The use of sequential measurements may be an option to achieve greater accuracy when ARS are used to monitor or assess the effects of physiotherapy interventions in this population.

Acoustic Monitoring of Night-Time Respiratory Symptoms in 14 Patients with Exacerbated COPD Over a 3- Week Period.

Introduction: In clinical practice, wheezing and coughing represent a worsening of the respiratory situation of COPD patients and should be monitored long-term during and after an Acute Exacerbation of COPD (AECOPD) to observe the therapy. We investigated if overnight monitoring of wheezing and coughing is feasible during AECOPD and whether automatic long - term monitoring enables an objective assessment during and after an AECOPD. Methods: In 14 patients (age: 56-80 years) with pre-existing COPD (stages B-D) nighttime wheezing and coughing events were monitored for a period of three weeks. The portable LEOSound® monitor recorded three nights into AECOPD (nights 1, 3 and 6) during the hospital stay, and the 20th night post- AECOPD ambulatory. Before each recording the subjective symptom severity was assessed by a COPD Assessment Test (CAT) and a Modified British Medical Research Council (MMRC) dyspnoea index questionnaire. Results: In all 14 patients, lung sounds were recorded in good quality during each of the 4 recording nights. Wheezing ranged between 5% and 90% (79 -539.5 minutes) of the recording time on the first night. All patients showed some coughs, in four patients coughing was particularly pronounced and largely receding over the total investigation period. As group, the percentages of wheezing and the number of coughs did not show significant differences between the four recording times. The CAT scores (p<0.001) declined over the course of investigation period, suggesting a subjective improvement of symptoms. Conclusion: The observational study showed that standardized long-term recording can be performed in high-quality during acute COPD exacerbation as it does not require the patient's cooperation. The good-quality data of coughs and wheezing were analyzed qualitatively and quantitatively. The long-term presentation of respiratory symptoms during an AECOPD offers the opportunity to evaluate factors that influence exacerbations and therapeutic approaches.

Wide-Bandwidth Nanocomposite-Sensor Integrated Smart Mask for Tracking Multiphase Respiratory Activities.

Wearing masks has been a recommended protective measure due to the risks of coronavirus disease 2019 (COVID-19) even in its coming endemic phase. Therefore, deploying a "smart mask" to monitor human physiological signals is highly beneficial for personal and public health. This work presents a smart mask integrating an ultrathin nanocomposite sponge structure-based soundwave sensor (≈400 µm), which allows the high sensitivity in a wide-bandwidth dynamic pressure range, i.e., capable of detecting various respiratory sounds of breathing, speaking, and coughing. Thirty-one subjects test the smart mask in recording their respiratory activities. Machine/deep learning methods, i.e., support vector machine and convolutional neural networks, are used to recognize these activities, which show average macro-recalls of ≈95% in both individual and generalized models. With rich high-frequency (≈4000 Hz) information recorded, the two-/tri-phase coughs can be mapped while speaking words can be identified, demonstrating that the smart mask can be applicable as a daily wearable Internet of Things (IoT) device for respiratory disease identification, voice interaction tool, etc. in the future. This work bridges the technological gap between ultra-lightweight but high-frequency response sensor material fabrication, signal transduction and processing, and machining/deep learning to demonstrate a wearable device for potential applications in continual health monitoring in daily life.

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.

Remote Analysis of Respiratory Sounds in Patients With COVID-19: Development of Fast Fourier Transform-Based Computer-Assisted Diagnostic Methods.

BACKGROUND: Respiratory sounds have been recognized as a possible indicator of behavior and health. Computer analysis of these sounds can indicate characteristic sound changes caused by COVID-19 and can be used for diagnostics of this illness. OBJECTIVE: The aim of the study is to develop 2 fast, remote computer-assisted diagnostic methods for specific acoustic phenomena associated with COVID-19 based on analysis of respiratory sounds. METHODS: Fast Fourier transform (FFT) was applied for computer analysis of respiratory sound recordings produced by hospital doctors near the mouths of 14 patients with COVID-19 (aged 18-80 years) and 17 healthy volunteers (aged 5-48 years). Recordings for 30 patients and 26 healthy persons (aged 11-67 years, 34, 60%, women), who agreed to be tested at home, were made by the individuals themselves using a mobile telephone; the records were passed for analysis using WhatsApp. For hospitalized patients, the illness was diagnosed using a set of medical methods; for outpatients, polymerase chain reaction (PCR) was used. The sampling rate of the recordings was from 44 to 96 kHz. Unlike usual computer-assisted diagnostic methods for illnesses based on respiratory sound analysis, we proposed to test the high-frequency part of the FFT spectrum (2000-6000 Hz). RESULTS: Comparing the FFT spectra of the respiratory sounds of patients and volunteers, we developed 2 computer-assisted methods of COVID-19 diagnostics and determined numerical healthy-ill criteria. These criteria were independent of gender and age of the tested person. CONCLUSIONS: The 2 proposed computer-assisted diagnostic methods, based on the analysis of the respiratory sound FFT spectra of patients and volunteers, allow one to automatically diagnose specific acoustic phenomena associated with COVID-19 with sufficiently high diagnostic values. These methods can be applied to develop noninvasive screening self-testing kits for COVID-19.

Regional respiratory sound abnormalities in pneumothorax and pleural effusion detected via respiratory sound visualization and quantification: case report.

Assessment of respiratory sounds by auscultation with a conventional stethoscope is subjective. We developed a continuous monitoring and visualization system that enables objectively and quantitatively visualizing respiratory sounds. We herein present two cases in which the system showed regional differences in the respiratory sounds. We applied our novel continuous monitoring and visualization system to evaluate respiratory abnormalities in patients with acute chest disorders. Respiratory sounds were continuously recorded to assess regional changes in respiratory sound volumes. Because we used this system as a pilot study, the results were not shown in real time and were retrospectively analyzed. Case 1 An 89-year-old woman was admitted to our hospital for sudden-onset respiratory distress and hypoxia. Chest X-rays revealed left pneumothorax; thus, we drained the thorax. After confirming that the pneumothorax had improved, we attached the continuous monitoring and visualization system. Chest X-rays taken the next day showed exacerbation of the pneumothorax. Visual and quantitative findings showed a decreased respiratory volume in the left lung after 3 h. Case 2 A 94-year-old woman was admitted to our hospital for dyspnea. Chest X-rays showed a large amount of pleural effusion on the right side. The continuous monitoring and visualization system visually and quantitatively revealed a decreased respiratory volume in the lower right lung field compared with that in the lower left lung field. Our newly developed continuous monitoring and visualization system enabled quantitatively and visually detecting regional differences in respiratory sounds in patients with pneumothorax and pleural effusion.