SonicGuard Sensor-A Multichannel Acoustic Sensor for Long-Term Monitoring of Abdominal Sounds Examined through a Qualification Study.

Auscultation is a fundamental diagnostic technique that provides valuable diagnostic information about different parts of the body. With the increasing prevalence of digital stethoscopes and telehealth applications, there is a growing trend towards digitizing the capture of bodily sounds, thereby enabling subsequent analysis using machine learning algorithms. This study introduces the SonicGuard sensor, which is a multichannel acoustic sensor designed for long-term recordings of bodily sounds. We conducted a series of qualification tests, with a specific focus on bowel sounds ranging from controlled experimental environments to phantom measurements and real patient recordings. These tests demonstrate the effectiveness of the proposed sensor setup. The results show that the SonicGuard sensor is comparable to commercially available digital stethoscopes, which are considered the gold standard in the field. This development opens up possibilities for collecting and analyzing bodily sound datasets using machine learning techniques in the future.

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obstructive pulmonary disease (COPD). Current diagnosis relies on subjective lung auscultation by physicians. Enabling this capability via a low-profile, objective wearable device for remote patient monitoring (RPM) could offer pre-emptive, accurate respiratory data to patients. With this goal as our aim, we used a low-profile accelerometer-based wearable system that utilizes deep learning to objectively detect wheezing along with respiration rate using a single sensor. The miniature patch consists of a sensitive wideband MEMS accelerometer and low-noise CMOS interface electronics on a small board, which was then placed on nine conventional lung auscultation sites on the patient's chest walls to capture the pulmonary-induced vibrations (PIVs). A deep learning model was developed and compared with a deterministic time-frequency method to objectively detect wheezing in the PIV signals using data captured from 52 diverse patients with respiratory diseases. The wearable accelerometer patch, paired with the deep learning model, demonstrated high fidelity in capturing and detecting respiratory wheezes and patterns across diverse and pertinent settings. It achieved accuracy, sensitivity, and specificity of 95%, 96%, and 93%, respectively, with an AUC of 0.99 on the test set-outperforming the deterministic time-frequency approach. Furthermore, the accelerometer patch outperforms the digital stethoscopes in sound analysis while offering immunity to ambient sounds, which not only enhances data quality and performance for computational wheeze detection by a significant margin but also provides a robust sensor solution that can quantify respiration patterns simultaneously.

Paciente varón de tres años con sibilancias y tos crónica / Three-Year-Old Male with Wheezing and Chronic Cough

Se presenta el caso de un niño de 3 años con diagnóstico de asma, rinitis alérgica, características craneofaciales dismórficas e infecciones respiratorias altas y bajas recurrentes, manejado como asma desde un inicio. Como parte del estudio de comorbilidades, se decide realizar una prueba del sudor que sale en rango intermedio y más tarde se encuentra una mutación, donde se obtiene un resultado positivo para una copia que se asocia a fibrosis quística. Se revisará el caso, así como el diagnóstico, clínica y tratamiento del síndrome metabólico relacionado con el regulador de conductancia transmembrana de fibrosis quística (CRMS).

We present the case of a 3-year-old boy with a diagnosis of asthma, allergic rhinitis, dysmorphic craniofacial characteristics and recurrent upper and lower respiratory infections, managed as asthma from the beginning. As part of the study of comorbidi-ties, it was decided to carry out a sweat test that came out in the intermediate range and later one mutation was found, where a positive result was obtained for a copy that is associated with cystic fibrosis. The case will be reviewed, as well as the diagnosis, symptoms and treatment of the metabolic syndrome related to the cystic fibrosis trans-membrane conductance regulator (CRMS).

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic methods for timely intervention. Digital biomarkers based on audio, acoustics, and sound from the upper and lower respiratory system, as well as the voice, have emerged as valuable indicators of respiratory functionality. Recent advancements in machine learning (ML) algorithms offer promising avenues for the identification and diagnosis of respiratory diseases through the analysis and processing of such audio-based biomarkers. An ever-increasing number of studies employ ML techniques to extract meaningful information from audio biomarkers. Beyond disease identification, these studies explore diverse aspects such as the recognition of cough sounds amidst environmental noise, the analysis of respiratory sounds to detect respiratory symptoms like wheezes and crackles, as well as the analysis of the voice/speech for the evaluation of human voice abnormalities. To provide a more in-depth analysis, this review examines 75 relevant audio analysis studies across three distinct areas of concern based on respiratory diseases' symptoms: (a) cough detection, (b) lower respiratory symptoms identification, and (c) diagnostics from the voice and speech. Furthermore, publicly available datasets commonly utilized in this domain are presented. It is observed that research trends are influenced by the pandemic, with a surge in studies on COVID-19 diagnosis, mobile data acquisition, and remote diagnosis systems.

Comparative study of respiratory sounds classification methods based on cepstral analysis and artificial neural networks.

In this paper, we investigated and evaluated various machine learning-based approaches for automatically detecting wheezing sounds. We conducted a comprehensive comparison of these proposed systems, assessing their classification performance through metrics such as Sensitivity, Specificity, and Accuracy. The main approach to developing a machine learning-based system for classifying respiratory sounds involved the combination of a technique for extracting features from an unknown input sound with a classification method to determine its belonging class. The characterization techniques used in this study are based on the cepstral analysis, which was extensively employed in the automatic speech recognition field. While MFCC (Mel-Frequency Cepstral Coefficients) feature extraction methods are commonly used in respiratory sounds classification, our study introduces a novelty by employing GFCC (Gammatone-Frequency Cepstral Coefficients) and BFCC (Bark-Frequency Cepstral Coefficients) for this purpose. For the classification task, we employed two types of neural networks: the MLP (Multilayer Perceptron), a feedforward neural network, and a variant of the LSTM (Long Short-Term Memory) recurrent neural network called BiLSTM (Bidirectional LSTM). The proposed classification systems are evaluated using a database consisting of 497 wheezing segments and 915 normal respiratory segments, which are recorded from individuals diagnosticated with asthma and individuals without any respiratory issues, respectively. The highest classification performance was achieved by the BFCC-BiLSTM model, which demonstrated an exceptional accuracy rate of 99.8%.

Intra-breath changes in respiratory mechanics are sensitive to history of respiratory illness in preschool children: the SEPAGES cohort.

BACKGROUND: Intra-breath oscillometry has been proposed as a sensitive means of detecting airway obstruction in young children. We aimed to assess the impact of early life wheezing and lower respiratory tract illness on lung function, using both standard and intra-breath oscillometry in 3 year old children. METHODS: History of doctor-diagnosed asthma, wheezing, bronchiolitis and bronchitis and hospitalisation for respiratory problems were assessed by questionnaires in 384 population-based children. Association of respiratory history with standard and intra-breath oscillometry parameters, including resistance at 7 Hz (R7), frequency-dependence of resistance (R7 - 19), reactance at 7 Hz (X7), area of the reactance curve (AX), end-inspiratory and end-expiratory R (ReI, ReE) and X (XeI, XeE), and volume-dependence of resistance (ΔR = ReE-ReI) was estimated by linear regression adjusted on confounders. RESULTS: Among the 320 children who accepted the oscillometry test, 281 (88%) performed 3 technically acceptable and reproducible standard oscillometry measurements and 251 children also performed one intra-breath oscillometry measurement. Asthma was associated with higher ReI, ReE, ΔR and R7 and wheezing was associated with higher ΔR. Bronchiolitis was associated with higher R7 and AX and lower XeI and bronchitis with higher ReI. No statistically significant association was observed for hospitalisation. CONCLUSIONS: Our findings confirm the good success rate of oscillometry in 3-year-old children and indicate an association between a history of early-life wheezing and lower respiratory tract illness and lower lung function as assessed by both standard and intra-breath oscillometry. Our study supports the relevance of using intra-breath oscillometry parameters as sensitive outcome measures in preschool children in epidemiological cohorts.

Breath characteristics and adventitious lung sounds in healthy and asthmatic horses.

BACKGROUND: Standard thoracic auscultation suffers from limitations, and no systematic analysis of breath sounds in asthmatic horses exists. OBJECTIVES: First, characterize breath sounds in horses recorded using a novel digital auscultation device (DAD). Second, use DAD to compare breath variables and occurrence of adventitious sounds in healthy and asthmatic horses. ANIMALS: Twelve healthy control horses (ctl), 12 horses with mild to moderate asthma (mEA), 10 horses with severe asthma (sEA) (5 in remission [sEA-], and 5 in exacerbation [sEA+]). METHODS: Prospective multicenter case-control study. Horses were categorized based on the horse owner-assessed respiratory signs index. Each horse was digitally auscultated in 11 locations simultaneously for 1 hour. One-hundred breaths per recording were randomly selected, blindly categorized, and statistically analyzed. RESULTS: Digital auscultation allowed breath sound characterization and scoring in horses. Wheezes, crackles, rattles, and breath intensity were significantly more frequent, higher (P < .001, P < .01, P = .01, P < .01, respectively) in sEA+ (68.6%, 66.1%, 17.7%, 97.9%, respectively), but not in sEA- (0%, 0.7%, 1.3%, 5.6%) or mEA (0%, 1.0%, 2.4%, 1.7%) horses, compared to ctl (0%, 0.6%, 1.8%, -9.4%, respectively). Regression analysis suggested breath duration and intensity as explanatory variables for groups, wheezes for tracheal mucus score, and breath intensity and wheezes for the 23-point weighted clinical score (WCS23). CONCLUSIONS AND CLINICAL IMPORTANCE: The DAD permitted characterization and quantification of breath variables, which demonstrated increased adventitious sounds in sEA+. Analysis of a larger sample is needed to determine differences among ctl, mEA, and sEA- horses.

Classification of Adventitious Sounds Combining Cochleogram and Vision Transformers.

Early identification of respiratory irregularities is critical for improving lung health and reducing global mortality rates. The analysis of respiratory sounds plays a significant role in characterizing the respiratory system's condition and identifying abnormalities. The main contribution of this study is to investigate the performance when the input data, represented by cochleogram, is used to feed the Vision Transformer (ViT) architecture, since this input-classifier combination is the first time it has been applied to adventitious sound classification to our knowledge. Although ViT has shown promising results in audio classification tasks by applying self-attention to spectrogram patches, we extend this approach by applying the cochleogram, which captures specific spectro-temporal features of adventitious sounds. The proposed methodology is evaluated on the ICBHI dataset. We compare the classification performance of ViT with other state-of-the-art CNN approaches using spectrogram, Mel frequency cepstral coefficients, constant-Q transform, and cochleogram as input data. Our results confirm the superior classification performance combining cochleogram and ViT, highlighting the potential of ViT for reliable respiratory sound classification. This study contributes to the ongoing efforts in developing automatic intelligent techniques with the aim to significantly augment the speed and effectiveness of respiratory disease detection, thereby addressing a critical need in the medical field.

Computerized respiratory sound based diagnosis of pneumonia.

Globally, respiratory disorders are a great health burden, affecting as well as destroying human lives; pneumonia is one among them. Pneumonia stages can progress from mild stage to even towards deadly if it is misdiagnosed. Misdiagnosis happens as it exhibits the symptoms identical to other respiratory diseases. Respiratory sound (RS)-based detection of pneumonia could be the most perfect, convenient, as well as the economical solution to this serious problem. This paper presents a novel method to detect pneumonia based on RS. This study is carried out over 310 pneumonia RS and 318 healthy RS, recorded from a hospital. The noises from each RS are eliminated using the Butterworth band pass filter and sparsity-assisted signal smoothing algorithm. Approximate entropy, Shannon entropy, fractal dimension, and largest Lyapunov exponent are the nonlinear features, which are extracted from each denoised RS. The extracted features are inputted to support vector machine classifiers to distinguish pneumonia RS and healthy RS. This method discriminates against pneumonia and healthy RS with 99.8% classification accuracy, 99.8% sensitivity, 99.6% specificity, 99.6% positive predictive value, 99.6% F1-score, and area under curve value of 1.0. Future endeavours will be to examine the efficacy of the proposed algorithm to diagnose pneumonia from the real-time RS acquired from a pneumonia patient in a hospital. This proposed work could be a great support to clinicians in diagnosing pneumonia based on RS.

Study on the flow mechanism and frequency characteristics of rales in lower respiratory tract.

The frequency characteristics of lung sounds have great significance for noninvasive diagnosis of respiratory diseases. The rales in the lower respiratory tract region that can provide rich information about symptoms of respiratory diseases are not clear. In this paper, a three-dimensional idealized bifurcated lower respiratory tract geometric model, which contains 3rd to 13th generation (G3-G13) bronchi is constructed, where Re ∼ 10 1 - 10 3 , and then the large eddy simulation and volume of fluid are used to study the fluid flow characteristics. Ffowcs Williams and Hawkings model are subsequently used to study the frequency characteristics of rale of different generations of bronchi. The results showed that bronchial blockage and sputum movement will enhance the turbulence intensity and vortex shedding intensity of flow. The dominant frequency and highest value of sound pressure level (SPL) of rhonchi/moist crackles decrease with the increase of bronchial generation. The change rates of dominant frequency of rhonchi / moist crackles in adjacent generations were 5.0 ± 0.1 ~ 9.1 ± 0.2% and 3.1 ± 0.1 ~ 11.9 ± 0.3%, respectively, which is concentrated in 290 ~ 420 Hz and 200 ~ 300 Hz, respectively. The change rates of SPL of rhonchi/moist crackles were 8.8 ± 0.1 ~ 15.7 ± 0.1% and 7.1 ± 0.1 ~ 19.5 ± 0.2%, respectively, which is concentrated in 28 ~ 50 dB and 16 ~ 32 dB, respectively. In the same generation of bronchus (e.g., G8, G9) with the same degree of initial blockage, the dominant frequency and SPL of moist crackles can be 3.7 ± 0.2% and 4.5 ± 0.3% slightly higher than that of rhonchi, respectively. This research is conducive to the establishment of a rapid and accurate noninvasive diagnosis system for respiratory diseases.

Palivizumab prophylaxis in preterm infants and subsequent wheezing/asthma: 10-year follow-up study.

BACKGROUND: Respiratory syncytial virus (RSV) causes not only infantile recurrent wheezing but also the development of asthma. To investigate whether palivizumab, an anti-RSV monoclonal antibody, prophylaxis given to preterm infants during the first RSV season reduces the incidence of subsequent recurrent wheezing and/or development of asthma, at 10 years of age. METHODS: We conducted an observational prospective multicenter (52 registered hospitals in Japan) case-control study in preterm infants with a gestational age between 33 and 35 weeks followed for 6 years. During the 2007-2008 RSV season, the decision to administer palivizumab was made based on standard medical practice (SCELIA study). Here, we followed these subjects until 10 years of age. Parents of study subjects reported the patients' physician's assessment of recurrent wheezing/asthma, using a report card and a novel mobile phone-based reporting system using the internet. The relationship between RSV infection and asthma development, as well as the relationship between other factors and asthma development, were investigated. RESULTS: Of 154 preterm infants enrolled, 113 received palivizumab during the first year of life. At 10 years, although both recurrent wheezing and development of asthma were not significantly different between the treated and untreated groups, maternal smoking with aeroallergen sensitization of the patients was significantly correlated with physician-diagnosed asthma. CONCLUSIONS: In contrast to the prior study results at 6 years, by 10 years palivizumab prophylaxis had no impact on recurrent wheezing or asthma, but there was a significant correlation between maternal passive smoking with aeroallergen sensitization and development of asthma by 10 years of age.

Constrained Synthetic Sampling for Augmentation of Crackle Lung Sounds.

Crackles are explosive breathing patterns caused by lung air sacs filling with fluid and act as an indicator for a plethora of pulmonary diseases. Clinical studies suggest a strong correlation between the presence of these adventitious auscultations and mortality rate, especially in pediatric patients, underscoring the importance of their pathological indication. While clinically important, crackles occur rarely in breathing signals relative to other phases and abnormalities of lung sounds, imposing a considerable class imbalance in developing learning methodologies for automated tracking and diagnosis of lung pathologies. The scarcity and clinical relevance of crackle sounds compel a need for exploring data augmentation techniques to enrich the space of crackle signals. Given their unique nature, the current study proposes a crackle-specific constrained synthetic sampling (CSS) augmentation that captures the geometric properties of crackles across different projected object spaces. We also outline a task-agnostic validation methodology that evaluates different augmentation techniques based on their goodness of fit relative to the space of original crackles. This evaluation considers both the separability of the manifold space generated by augmented data samples as well as a statistical distance space of the synthesized data relative to the original. Compared to a range of augmentation techniques, the proposed constrained-synthetic sampling of crackle sounds is shown to generate the most analogous samples relative to original crackle sounds, highlighting the importance of carefully considering the statistical constraints of the class under study.

Abnormal Respiratory Sound Identification Using Audio-Spectrogram Vision Transformer.

Respiratory disease, the third leading cause of deaths globally, is considered a high-priority ailment requiring significant research on identification and treatment. Stethoscope-recorded lung sounds and artificial intelligence-powered devices have been used to identify lung disorders and aid specialists in making accurate diagnoses. In this study, audio-spectrogram vision transformer (AS-ViT), a new approach for identifying abnormal respiration sounds, was developed. The sounds of the lungs are converted into visual representations called spectrograms using a technique called short-time Fourier transform (STFT). These images are then analyzed using a model called vision transformer to identify different types of respiratory sounds. The classification was carried out using the ICBHI 2017 database, which includes various types of lung sounds with different frequencies, noise levels, and backgrounds. The proposed AS-ViT method was evaluated using three metrics and achieved 79.1% and 59.8% for 60:40 split ratio and 86.4% and 69.3% for 80:20 split ratio in terms of unweighted average recall and overall scores respectively for respiratory sound detection, surpassing previous state-of-the-art results.

[New progress in diagnosis and treatment of congenital laryngomalacia in infants].

Congenital laryngomalacia is the most common disease causing laryngeal stridor in infants. The pathogenesis has not yet been clearly concluded. It may be related to abnormal development of laryngeal cartilage anatomical structure, neuromuscular dysfunction, gastroesophageal and laryngeal reflux disease, etc. The typical manifestations of the disease are inspiratory laryngeal stridor and feeding difficulties, which can be divided into mild, moderate and severe according to the severity of symptoms. The diagnosis is mainly based on clinical symptoms, signs and endoscopy, among which endoscopy is an important diagnostic basis. The treatment of laryngomalacia depends on the severity of symptoms. Mild and some moderate congenital laryngomalacia children can be relieved by conservative treatment, and severe and some moderate congenital laryngomalacia children should be treated by surgery. Supraglottic plasty is the main surgical method, which can effectively improve the symptoms of laryngeal stridor, dyspnea, feeding difficulties and growth retardation in most children, and the surgical effect is good.

Phenotypes of cough in children: A latent class analysis.

INTRODUCTION: Distinguishing phenotypes among children with cough helps understand underlying causes. Using a statistical data-driven approach, we aimed to identify and validate cough phenotypes based on measurable traits, physician diagnoses, and prognosis. METHODS: We used data from the Swiss Paediatric Airway Cohort and included 531 children aged 5-16 years seen in outpatient clinics since 2017. We included children with any parent-reported cough (i.e. cough without a cold, cough at night, cough more than other children, or cough longer than 4 weeks) without current wheeze. We applied latent class analysis to identify phenotypes using nine symptoms and characteristics and selected the best model using the Akaike information criterion. We assigned children to the most likely phenotype and compared the resulting groups for parental atopy history, comorbidities, spirometry, fractional exhaled nitric oxide (FeNO), skin prick tests and specific IgE, physician diagnoses, and 1-year prognosis. RESULTS: We identified four cough phenotypes: non-specific cough (26%); non-allergic infectious and night cough with snoring and otitis (4%); chronic allergic dry night cough with snoring (9%); and allergic non-infectious cough with rhino-conjunctivitis (61%). Children with the allergic phenotype often had family or personal history of atopy and asthma diagnosis. FeNO was highest for the allergic phenotype [median 17.9 parts per billion (ppb)] and lowest for the non-allergic infectious phenotype [median 7.0 parts per billion (ppb)]. Positive allergy test results differed across phenotypes (p < .001) and were most common among the allergic (70%) and least common among the non-specific cough (31%) phenotypes. Subsequent wheeze was more common among the allergic than the non-specific phenotype. CONCLUSION: We identified four clinically relevant cough phenotypes with different prognoses. Although we excluded children with current wheeze, most children with cough belonged to allergy-related phenotypes.

BrachySound: machine learning based assessment of respiratory sounds in dogs.

The early and accurate diagnosis of brachycephalic obstructive airway syndrome (BOAS) in dogs is pivotal for effective treatment and enhanced canine well-being. Owners often do underestimate the severity of BOAS in their dogs. In addition, traditional diagnostic methods, which include pharyngolaryngeal auscultation, are often compromised by subjectivity, are time-intensive and depend on the veterinary surgeon's experience. Hence, new fast, reliable assessment methods for BOAS are required. The aim of the current study was to use machine learning techniques to bridge this scientific gap. In this study, machine learning models were employed to objectively analyze 366 audio samples from 69 Pugs and 79 other brachycephalic breeds, recorded with an electronic stethoscope during a 15-min standardized exercise test. In classifying the BOAS test results as to whether the dog is affected or not, our models achieved a peak accuracy of 0.85, using subsets from the Pugs dataset. For predictions of the BOAS results from recordings at rest in Pugs and various brachycephalic breeds, accuracies of 0.68 and 0.65 were observed, respectively. Notably, the detection of laryngeal sounds achieved an F1 score of 0.80. These results highlight the potential of machine learning models to significantly streamline the examination process, offering a more objective assessment than traditional methods. This research indicates a turning point towards a data-driven, objective, and efficient approach in canine health assessment, fostering standardized and objective BOAS diagnostics.

Real-time counting of wheezing events from lung sounds using deep learning algorithms: Implications for disease prediction and early intervention.

This pioneering study aims to revolutionize self-symptom management and telemedicine-based remote monitoring through the development of a real-time wheeze counting algorithm. Leveraging a novel approach that includes the detailed labeling of one breathing cycle into three types: break, normal, and wheeze, this study not only identifies abnormal sounds within each breath but also captures comprehensive data on their location, duration, and relationships within entire respiratory cycles, including atypical patterns. This innovative strategy is based on a combination of a one-dimensional convolutional neural network (1D-CNN) and a long short-term memory (LSTM) network model, enabling real-time analysis of respiratory sounds. Notably, it stands out for its capacity to handle continuous data, distinguishing it from conventional lung sound classification algorithms. The study utilizes a substantial dataset consisting of 535 respiration cycles from diverse sources, including the Child Sim Lung Sound Simulator, the EMTprep Open-Source Database, Clinical Patient Records, and the ICBHI 2017 Challenge Database. Achieving a classification accuracy of 90%, the exceptional result metrics encompass the identification of each breath cycle and simultaneous detection of the abnormal sound, enabling the real-time wheeze counting of all respirations. This innovative wheeze counter holds the promise of revolutionizing research on predicting lung diseases based on long-term breathing patterns and offers applicability in clinical and non-clinical settings for on-the-go detection and remote intervention of exacerbated respiratory symptoms.

Assessing asthma symptoms in children: qualitative research supporting the development of the Pediatric Asthma Diary-Child (PAD-C) and Pediatric Asthma Diary-Observer (PAD-O).

BACKGROUND: Pediatric asthma has been identified by regulators, clinicians, clinical trial sponsors, and caregivers as an area in need of novel fit-for-purpose clinical outcome assessments (COAs) developed in accordance with the U.S. Food and Drug Administration's (FDA's) regulatory guidance for evaluating clinical benefit in treatment trials. To address this gap, the Patient-Reported Outcome (PRO) Consortium's Pediatric Asthma Working Group has continued development of 2 COAs to assess asthma signs and symptoms in pediatric asthma clinical trials to support efficacy endpoints: a PRO measure, the Pediatric Asthma Diary-Child (PAD-C) for children 8-11 years old (y.o.) and an observer-reported outcome measure, the Pediatric Asthma Diary-Observer (PAD-O) for caregivers of children 4-11 y.o. This qualitative research aimed to generate evidence regarding the content validity of the PAD-C and PAD-O. METHODS: Semi-structured combined concept elicitation and cognitive interviews were conducted with a diverse sample of U.S. participants (15 children 8-11 y.o. and 30 caregivers of children 4-11 y.o.). All children had clinician-diagnosed mild to severe asthma. Interviews explored the experience of pediatric asthma and assessed the understanding and relevance of both measures. Interviews were conducted across 3 iterative rounds to allow for modifications. RESULTS: Concept elicitation findings demonstrated that the core sign/symptom and impact concepts assessed in the PAD-C (cough, hard to breathe, out of breath, wheezing, chest tightness, and nighttime awakenings/symptoms) and PAD-O (cough, difficulty breathing, short of breath, wheezing, and nighttime awakenings/signs) correspond to those most frequently reported by participants; concept saturation was achieved. All PAD-C and PAD-O instructions and core items were well understood and considered relevant by most participants. Feedback from participants, the Pediatric Asthma Working Group, advisory panel, and FDA supported modifications to the measures, including addition of 1 new item to both measures and removal of 1 caregiver item. CONCLUSIONS: Findings provide strong support for the content validity of both measures. The cross-sectional measurement properties of both measures and their user experience and feasibility in electronic format will be assessed in a future quantitative pilot study with qualitative exit interviews, intended to support the reliability, construct validity, final content, and, ultimately, FDA qualification of the measures.

Pediatric asthma is one of the most common chronic diseases in children. However, there are problems of underdiagnosis, poor disease management, and undertreatment for many pediatric asthma patients, pressuring healthcare systems worldwide. Evaluating asthma symptoms is an important part of the development of treatments for pediatric asthma. However, there are few clinical outcome assessments (COAs) developed in line with regulatory guidance to directly assess symptom severity and evaluate the benefit of new treatments in children with asthma. In this study, we continued the development of the Pediatric Asthma Diary­Child (PAD-C) and the Pediatric Asthma Diary­Observer (PAD-O), according to regulatory guidance, to assess asthma signs and symptoms in children 4 through 11 years old and address this unmet need. The study aimed to explore the experience of pediatric asthma and assess how well-understood and relevant the measures are. Three rounds of qualitative interviews were conducted with 15 children 8 through 11 years old and 30 caregivers of children 4 through 11 years old with asthma. Results show that both measures are well-understood and assess the relevant and important aspects of pediatric asthma reported by children and caregivers. Findings provide evidence supporting the PAD-C and PAD-O as measures of symptom severity and their future use in pediatric asthma treatment trials. Further research is underway to evaluate their measurement properties and assess the user experience and feasibility of electronic completion, to ultimately support the PAD-C and PAD-O in an ongoing COA qualification process by the United States Food and Drug Administration.

Transformer-Based Network for Accurate Classification of Lung Auscultation Sounds.

Respiratory diseases are a major cause of death worldwide, affecting a significant proportion of the population with lung function abnormalities that can lead to respiratory illnesses. Early detection and prevention are critical to effective management of these disorders. Deep learning algorithms offer a promising approach for analyzing complex medical data and aiding in early disease detection. While transformer-based models for sequence classification have proven effective for tasks like sentiment analysis, topic classification, etc., their potential for respiratory disease classification remains largely unexplored. This paper proposes a classifier utilizing the transformer-encoder block, which can capture complex patterns and dependencies in medical data. The proposed model is trained and evaluated on a large dataset from the International Conference on Biomedical Health Informatics 2017, achieving state-of-the-art results with a mean sensitivity of 70.53%, mean specificity of 84.10%, mean average score of 77.32%, and mean harmonic score of 76.10%. These results demonstrate the model's effectiveness in diagnosing respiratory diseases while taking up minimal computational resources.