Breath Sounds Research Articles

Early Diagnosis of Pneumonia and Chronic Obstructive Pulmonary Disease with a Smart Stethoscope with Cloud Server-Embedded Machine Learning in the Post-COVID-19 Era

Background/Objectives: Respiratory diseases are common and result in high mortality, especially in the elderly, with pneumonia and chronic obstructive pulmonary disease (COPD). Auscultation of lung sounds using a stethoscope is a crucial method for diagnosis, but it may require specialized training and the involvement of pulmonologists. This study aims to assist medical professionals who are non-pulmonologist doctors in early screening for pneumonia and COPD by developing a smart stethoscope with cloud server-embedded machine learning to diagnose lung sounds. Methods: The smart stethoscope was developed using a Micro-Electro-Mechanical system (MEMS) microphone to record lung sounds in the mobile application and then send them wirelessly to a cloud server for real-time machine learning classification. Results: The model of the smart stethoscope classifies lung sounds into four categories: normal, pneumonia, COPD, and other respiratory diseases. It achieved an accuracy of 89%, a sensitivity of 89.75%, and a specificity of 95%. In addition, testing with healthy volunteers yielded an accuracy of 80% in distinguishing normal and diseased lungs. Moreover, the performance comparison between the smart stethoscope and two commercial auscultation stethoscopes showed comparable sound quality and loudness results. Conclusions: The smart stethoscope holds great promise for improving healthcare delivery in the post-COVID-19 era, offering the probability of the most likely respiratory conditions for early diagnosis of pneumonia, COPD, and other respiratory diseases. Its user-friendly design and machine learning capabilities provide a valuable resource for non-pulmonologist doctors by delivering timely, evidence-based diagnoses, aiding treatment decisions, and paving the way for more accessible respiratory care.

Artificial intelligence in paediatric pneumology - opportunities and unanswered questions

Artificial intelligence (AI) is already being used in most medical disciplines, including paediatric pneumology. This review describes current developments in AI-supported technologies and discusses their potential for the diagnosis and treatment of lung diseases in children and adolescents. The spectrum ranges from models for analysing respiratory sounds and the automated evaluation of medical imaging to systems for supporting clinical decisions. In particular, challenges in the adaptation of AI for paediatric populations are also described. Finally, open questions, such as the implementation of AI-based software in everyday clinical practice, will be discussed.

Detection of breath sounds in speech: A deep learning approach

Detection of breath sounds in speech: A deep learning approach

A Robust Approach for Pulmonary Disease Diagnosis with Multifractal Features of lung sounds utilizing Machine Learning models

A Robust Approach for Pulmonary Disease Diagnosis with Multifractal Features of lung sounds utilizing Machine Learning models

P-1189. Clinical Characteristics and Outcomes of Medically Attended Respiratory Syncytial Virus (RSV) Infections in Children 24-59 months of Age

Abstract Background RSV infection is a major cause of lower respiratory tract disease (LRTD) among young children. However, clinical features of RSV disease in children ≥24 months of age are not well described. This study assessed the signs, symptoms, and clinical characteristics of RSV in children 24-59 months of age in both outpatient and inpatient settings. Methods We abstracted clinical records from RSV-positive children 24-59 months of age who sought outpatient care for acute respiratory illness (ARI) and who had provided a respiratory swab for RSV testing by RT-PCR as part of influenza vaccine effectiveness studies from 2004-10 and 2014-15. Records were also abstracted for hospitalized children with laboratory-confirmed RSV from 2010-23. We analyzed clinical features of RSV in each care setting. Results We identified 580 pediatric patients 24-59 months with medically attended, laboratory-confirmed RSV infection, including 419 outpatient and 161 inpatient encounters. The prevalence of comorbidities was 31% among outpatients and 66% among inpatients. The median hospital stay was 3.0 days (IQR: 2.0 - 4.3), and 22% were admitted to the pediatric intensive care unit (PICU). Abnormal lung sounds were observed in 21% of outpatients and 70% of hospitalized patients (P< 0.001). Shortness of breath was documented in 6% of outpatients and in 71% of inpatients (P< 0.001). Increased respiratory rate was recorded in 10% of outpatients and in 48% of inpatients (P< 0.001). The median oxygen saturation at the first measurement for outpatients was 96% (IQR: 95%–97%), compared to 93% (IQR: 90%–95%) for inpatients (P < 0.001). In the complete dataset, 25% of outpatients and 93% of hospitalized children aged 24-59 months exhibited signs or symptoms of LRTD, such as retractions, abnormal lung sounds, hypoxemia and/or need for supplemental oxygen. Conclusion Among children 24-59 months of age with medically attended RSV infection, two-thirds of outpatient visits and one-third of inpatient visits occurred in previously healthy children. Nearly one-quarter of hospitalized children required PICU admission, indicating significant morbidity in this age group. Further research on the epidemiology of RSV in children outside of infancy is needed. Disclosures Zhe Zheng, PhD, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Jennifer P. King, MPH, GSK Inc.: Grant/Research Support|ModernaTX: Grant/Research Support Linwei Li, n/a, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Tianyu Sun, n/a, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Catherine A. Panozzo, PhD, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Thomas G. Boyce, MD, GSK: Grant/Research Support|Moderna, Inc.: Grant/Research Support Iliana Leony Lasso, n/a, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Sabine Schnyder Ghamloush, MD, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Sonia K. Stoszek, PhD, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Matthew D. Snape, MBBS, MD, FRCPCH, FPM, FMedSci, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Evan J. Anderson, MD, Moderna, Inc.: Employee|Moderna, Inc.: Stocks/Bonds (Public Company) Huong McLean, PhD, MPH, CSL Seqirus: Advisor/Consultant|CSL Seqirus: Grant/Research Support|GSK: Grant/Research Support|ModernaTX: Advisor/Consultant|ModernaTX: Grant/Research Support Joshua Petrie, PhD, CSL Seqirus: Grant/Research Support|Moderna, Inc.: Grant/Research Support

O-05 A RARE CAUSE OF BILATERAL GIANT ADRENAL MASS: PRIMARY ADRENAL LYMPHOMA

Abstract Introduction Primary adrenal lymphoma (PAL) is an extremely rare form of non-Hodgkin lymphoma, representing less than 1% of cases. It is usually bilateral, unlike secondary adrenal lymphoma, which is often unilateral. Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of PAL, making up about 70% of cases. PAL primarily affects older patients and lacks distinct nodular lesions, making diagnosis difficult; pathological examination is essential for confirmation. Clinical Case A 62-year-old male patient with no known comorbidities presented with symptoms of night sweats, weight loss (approximately 10 kg over the past month), and general weakness persisting for the past two months. Physical examination revealed decreased breath sounds in the bilateral lower lung fields and a dullness to percussion over Traube's space on abdominal examination. No other pathological findings were noted. Vital signs were in normal range. Laboratory evaluation showed normocytic anemia, thrombocytopenia, and hypercalcemia with suppressed parathyroid hormone levels. Additionally, lactate dehydrogenase levels were elevated (see Table 1). A thoracic, abdominal, and pelvic computed tomography (CT) scan was performed to assess for malignancy. The CT scan revealed a solid mass lesion measuring approximately 11 x 18 x 10 cm with an average density of 50 HU, exhibiting non-homogeneous contrast enhancement. This lesion is located in the bilateral adrenal regions and show confluent extension anterior to the abdominal aorta (see Figure 1). Subsequently, the patient underwent hormonal analysis to assess adrenal function and an 18F-FDG PET/CT scan was planned. Hormonal analysis indicated that the adrenal mass was non-functional, with preserved adrenal gland function (see Table 2). The PET/CT scan showed intense pathological activity accumulation in the adrenal mass lesion (SUVmax: 25.9) and diffuse increased activity in the bone marrow. Following this, both bone marrow and adrenal mass biopsies were performed. The bone marrow biopsy did not reveal infiltrative pathology. However, the biopsy of the adrenal mass was reported as aggressive mature large B-cell lymphoma. Primary bilateral adrenal lymphoma was diagnosed. The patient was evaluated by hematology and subsequently initiated on R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) chemotherapy. Following six cycles of R-CHOP, the suprarenal mass is completely resolved. However, during follow-up one year later, the disease relapsed with a cranial mass. Conclusion PAL is a rare extranodal non-Hodgkin's lymphoma, predominantly affecting males around 70 years old. It usually presents with large bilateral adrenal masses and can cause primary adrenal insufficiency (PAI) in up to 70% of cases. Diagnosis is confirmed by histology after imaging. Treatment typically involves a combination of surgery, chemotherapy, and/or radiotherapy, with PAI requiring hormone replacement therapy.Figure 1:Imaging of the Tumor Lesion Before and After Chemotherapya/b) Pre/Post-Chemotherapy PET/CT Imaging, c/d) Pre/Post-Chemotherapy CT Imaging Table 1:Patient’s laboratory parameters Table 2:Patient’s adrenal function assessment(Values marked with an asterisk (*) were measured in a 24-hour urine collection, while the other values were measured in plasma.) (DHEAS: Dehydroepiandrosterone sulfate)

Utilizing machine learning to predict hospital admissions for pediatric COVID-19 patients (PrepCOVID-Machine)

The COVID-19 pandemic has burdened healthcare systems globally. To curb high hospital admission rates, only patients with genuine medical needs are admitted. However, machine learning (ML) models to predict COVID-19 hospitalization in Asian children are lacking. This study aimed to develop and validate ML models to predict pediatric COVID-19 hospitalization. We collected secondary data with 2200 patients and 65 variables from Malaysian aged 0 to 12 with COVID-19 between 1st February 2020 and 31st March 2022. The sample was partitioned into training, internal, and external validation groups. Recursive Feature Elimination (RFE) was employed for feature selection, and we trained seven supervised classifiers. Grid Search was used to optimize the hyperparameters of each algorithm. The study analyzed 1988 children and 30 study variables after data were processed. The RFE algorithm selected 12 highly predicted variables for COVID-19 hospitalization, including age, male sex, fever, cough, rhinorrhea, shortness of breath, vomiting, diarrhea, seizures, body temperature, chest indrawing, and abnormal breath sounds. With external validation, Adaptive Boosting was the highest-performing classifier (AUROC = 0.95) to predict COVID-19 hospital admission in children. We validated AdaBoost as the best to predict COVID-19 hospitalization among children. This model may assist front-line clinicians in making medical disposition decisions.

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

Automated system for diagnosing pulmonary fibrosis using crackle analysis in recorded lung sounds based on iterative envelope mean fractal dimension filter.

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) pre-processing, (2) separation of crackles from normal breath sounds using the iterative envelope mean fractal dimension (IEM-FD) filter, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

Performance of individual criteria of the Pediatric Emergency Care Applied Research Network (PECARN) intraabdominal injury prediction rule.

The Pediatric Emergency Care Applied Research Network (PECARN) derived and externally validated a clinical prediction rule to identify children with blunt torso trauma at low risk for intraabdominal injuries undergoing acute intervention (IAIAI). Little is known about the risk for IAIAI when only one or two prediction rule variables are positive. We sought to determine the risk for IAIAI when either one or two PECARN intraabdominal injury rule variables are positive. We performed a planned secondary analysis of a prospective, multicenter study that included 7542 children (<18 years old) with blunt torso trauma evaluated in six emergency departments from December 2016 to August 2021. Patients with only one or two PECARN rule variables positive were included. The outcome was IAIAI (IAI undergoing therapeutic laparotomy, angiographic embolization, blood transfusion, or two or more nights of intravenous fluids). Among the 7542 children enrolled, 2986 (39.6%, 95% confidence interval [CI] 38.5%-40.7%) had one or two PECARN variables positive and were included. Of this subpopulation, 227 (7.6%, 95% CI 6.7%-8.6%) had intraabdominal injuries. In the 1639 patients with only one rule variable positive, 21 (1.3%, 95% CI 0.8%-2.0%) had IAIAI. In the 1347 patients with two rule variables positive, 27 (2.0%, 95% CI 1.3%-2.9%) had IAIAI. Risk for IAIAI for each variable was highest for Glasgow Coma Scale (GCS) score <14 (16/291, 5.5%, 95% CI 3.2%-8.8%) and abdominal wall trauma (three of 321, 0.9%, 95% CI 0.2%-2.7%). Risk for IAIAI when two variables were present was highest when decreased breath sounds (three of 44, 6.8%, 95% CI 1.4%-18.7%) and GCS <14 (10/207, 4.8%, 95% CI 2.3%-8.7%) were present with one other variable. Few children with blunt torso trauma and one or two PECARN predictor variables present have IAIAI. Those with GCS score <14, however, are at highest risk for IAIAI.

Clinical characteristics and risk factors of severe pneumonia caused by human bocavirus in children

BackgroundThe aim of this study was to investigate the clinical characteristics of severe pneumonia caused by human bocavirus (HBoV) infection to explore the associated risk factors.MethodsWe conducted a retrospective review of data from children hospitalized with HBoV pneumonia. Based on the severity of pneumonia, patients were categorized into severe pneumonia and non-severe pneumonia groups. Clinical manifestations, laboratory examination results, chest imaging and pathogens were analyzed. Logistic regression was employed to identify the risk factors for severe HBoV pneumonia.ResultsA total of 334 patients were admitted, with 44 (13.17%) patients diagnosed with severe pneumonia and 290 (86.83%) with non-severe pneumonia. There were no significant differences in age distribution, presence of fever, lung moist rales, pleural effusion and reduced breath sounds between the two groups (all P > 0.05). 57.19% of the HBoV-positive children co-infected with other pathogens and HRV was the most common co-infected pathogens with HBoV. No significant differences were observed in the rate of co-infection between the two groups (χ2 = 0.50, p = 0.48). The univariate analysis revealed significant differences between the severe pneumonia group and the non-severe group in terms of gender distribution, presence of underlying chronic diseases, wheezing, premature delivery, lung wheezing rales, pneumothorax, bronchoscopy procedures, length of hospital stay, duration of symptoms prior to admission, neutrophil count, CRP levels, CKMB levels, IgA levels, CD3+(%), CD3+CD4+(%), CD3+CD8+%, and CD3−CD19+% (all P < 0.05). Multivariate logistic regression analysis identified female gender, wheezing and neutrophil count were independent risk factors and the ratio of CD3+CD4+ cells was protective factor for severe HBoV pneumonia. The cut-off values of neutrophil count and the ratio of CD3+CD4+ cells were 6.81 × 109/L and 32.48 respectively.ConclusionOur study indicated that female gender, wheezing and neutrophil count greater than 6.81 × 109/L were independent risk factors and the ratio of CD3+CD4+ cells greater than 32.48 was protective factor for severe HBoV pneumonia.

Factors associated with mortality in thoracic trauma patients in Cameroon.

Thoracic trauma is the third most common cause of death after abdominal injury and head trauma in polytrauma patients. The identification of a patient at risk of thoracic trauma mortality is necessary to avoid delays that may lead to morbidity and mortality. Therefore, the objective was to assess the factors associated with mortality among thoracic trauma patients in Cameroon. This is a retrospective analysis of the Cameroon Trauma Registry currently running in 10 pilot hospital sites across seven of the ten regions. We retrieved data for all patients with thoracic trauma between the period June 2022 to May 2023 and analyzed factors associated to mortality using multivariate logistic regression. Statistical significance was set at p-value <0.05. Out of 4851 trauma patients, 642 (13.2%) presented with thoracic trauma. Over 80% were males and the majority [168 (26.2%)] were within the age group of 25-34 years. The most common injury mechanism was Road Traffic Injury (RTI). Overall thoracic trauma mortality rate was 9% with road traffic Injury being the commonest mechanism. Factors associated to thoracic trauma mortality were abnormal chest movement and respiration [(AOR=5.18, 95% CI=1.89-14.19, P=0.001) and (AOR=4.72, 95% CI=1.44-15.42, P=0.01) respectively]. Tracheal deviation, breath sounds and respiration were other factors associated with mortality. This analysis highlights thoracic trauma in Cameroon and its contribution to the overall injury death toll. Abnormal chest movement and respiration were factors associated with mortality among thoracic trauma patients. To improve the health outcomes, it is important to understand the treatment logic, involve thoracic surgeons and a multidisciplinary team to improve diagnosis and management.

Update on the theoretical and practical aspects of the evaluation of nursing care in patients with eclampsia and preeclampsia

Introduction: Eclampsia is a serious complication of pregnancy characterized by seizures in women with preeclampsia. The quality of nursing care is crucial for the effective management of eclamptic patients, as it influences maternal and neonatal outcomes.Objective: To update the theoretical framework and research background on the quality of nursing care in pregnant women with eclampsia and preeclampsia.Method: A narrative review of the updated literature was conducted using recognized databases.Development: The main nursing care is the constant evaluation and verification of signs of worsening of the disease. The fetus and the pregnant woman/newborn must be kept safe before, during, and after delivery. Nurses must assess respiratory rate and vital signs, respiratory sounds, oxygen saturation levels, deep tendon reflexes, intake and output, level of consciousness, and fetal status at frequent intervals.Conclusions: Staff training and compliance with protocols are essential to ensure safe and effective care. It is necessary to continue strengthening staff training and improving communication with patients to optimize results in the management of this critical condition

Mobile Application based Pulmonary Disease Prediction using Respiratory Sound and Deep Learning

Mobile Application based Pulmonary Disease Prediction using Respiratory Sound and Deep Learning

Etiology and Clinical Prediction of Community-Acquired Lower Respiratory Tract Infection in Children.

Community acquired lower respiratory tract infection (LRTI) is a leading cause for hospitalization in children and important cause for antibiotic prescription. We aimed to describe the aetiology of LRTI in children and analyse factors associated with bacterial or viral infection. Patients aged < 19 years with a diagnosis of LRTI were identified from the Observational Medical Outcomes Partnership Common Data Model Database of Seoul National University Bundang Hospital from January 2005-July 2019, and their clinical characteristics were obtained from the electronic medical records and retrospectively reviewed. Among 5,924 cases of LRTI, 74.2% were pneumonia and 25.8% were bronchiolitis/bronchitis. Patients' median age was 1.8 (interquartile range, 3.1) years and 79.9% were < 5 years old. Pathogens were identified in 37.8%; 69.1% were viral and 30.9% were bacterial/Mycoplasma pneumoniae. Respiratory syncytial virus was most common (70.9%) among viruses and M. pneumoniae (94.6%) was most common among bacteria. Viral LRTI was associated with winter, age < 2 years, rhinorrhoea, dyspnoea, lymphocytosis, thrombocytosis, wheezing, stridor, chest retraction, and infiltration on imaging. Bacteria/M. pneumoniae LRTI was associated with summer, age ≥ 2 years, fever, decreased breathing sounds, leucocytosis, neutrophilia, C-reactive protein elevation, and positive imaging findings (consolidation, opacity, haziness, or pleural effusion). In children with LRTI, various factors associated with viral or bacterial/M. pneumoniae infections were identified, which may serve as guidance for antibiotic prescription.

A Wearable Stethoscope for Accurate Real-Time Lung Sound Monitoring and Automatic Wheezing Detection Based on an AI Algorithm

A Wearable Stethoscope for Accurate Real-Time Lung Sound Monitoring and Automatic Wheezing Detection Based on an AI Algorithm

CT evaluation of pediatric airway foreign bodies: Emphasis on the role of CT scanning in foreign body removal with bronchoscopy

A foreign body that enters the airway through aspiration may become lodged in the trachea, bronchi, or larynx. The size, shape, and type of the foreign body determine where it should be lodged. Children are more frequently impacted. Unexpected inspiration during play or fighting while holding something in their mouths can result in accidents. The first study of choice for any child being evaluated for foreign body aspiration is still a plain film chest X-ray. Computerized tomographic examinations have a slightly higher sensitivity. We report a case of aspiration foreign body in a 12-year-old boy with the main complaint of choking. On physical examination, there were decreased breath sounds and wheezing in the upper left lung area. Chest x-ray revealed a radioopaque needle pin-like foreign body and no abnormalities in both lung. The results of a chest computed tomography (CT) scan showed a needle was abaout 4cm long and located in left bronchus. Once the diagnosis is made, removal of the foreign body with bronchoscopy is performed in these cases. Chest CT scans may need less time for evaluation and provide finer image resolution, making it easier to identify the position, size, and form of AFBs. As a result, chest CT scans offer great potential usefulness in the investigation of suspected AFBs.

Diaphragmatic Solitary Fibrous Pleura Tumor Masquerading as Lung Tumor- A Case Report

Background: Solitary pleural fibrous tumours of the diaphragm are rare and can be a diagnostic dilemma. Case presentation: A 49-year-old female presented to our clinic with a history of chronic cough with associated difficulty in breathing. Chest findings were dull percussion notes as well as reduced breath sounds on the left lower lung zone. Chest CT scan showed a left-sided intra-thoracic density with compression of the left lower lung lobe. She had a thoracotomy with the excision of a left diaphragmatic pleura tumour. Conclusion: This case intends to highlight that, though primary diaphragmatic pleura tumours are rare, they may present with non-specific symptoms and should be considered in evaluating patients with intra-thoracic tumours.

Dynamic Digital Radiography (DDR) in the Diagnosis of a Diaphragm Dysfunction.

Dynamic digital radiography (DDR) is a recent imaging technique that allows for real-time visualization of thoracic and pulmonary movement in synchronization with the breathing cycle, providing useful clinical information. A 46-year-old male, a former smoker, was evaluated for unexplained dyspnea and reduced exercise tolerance. His medical history included a SARS-CoV-2 infection in 2021. On physical examination, decreased breath sounds were noted at the right-lung base. Spirometry showed results below predicted values. A standard chest radiograph revealed an elevated right hemidiaphragm, a finding not present in a previous CT scan performed during his SARS-CoV-2 infection. To better assess the diaphragmatic function, a posteroanterior DDR study was performed in the standing position with X-ray equipment (AeroDR TX, Konica Minolta Inc., Tokyo, Japan) during forced breath, with the following acquisition parameters: tube voltage, 100 kV; tube current, 50 mA; pulse duration of pulsed X-ray, 1.6 ms; source-to-image distance, 2 m; additional filter, 0.5 mm Al + 0.1 mm Cu. The exposure time was 12 s. The pixel size was 388 × 388 μm, the matrix size was 1024 × 768, and the overall image area was 40 × 30 cm. The dynamic imaging, captured at 15 frames/s, was then assessed on a dedicated workstation (Konica Minolta Inc., Tokyo, Japan). The dynamic acquisition showed a markedly reduced motion of the right diaphragm. The diagnosis of diaphragm dysfunction can be challenging due to its range of symptoms, which can vary from mild to severe dyspnea. The standard chest X-ray is usually the first exam to detect an elevated hemidiaphragm, which may suggest motion impairment or paralysis but fails to predict diaphragm function. Ultrasound (US) allows for the direct visualization of the diaphragm and its motion. Still, its effectiveness depends highly on the operator's experience and could be limited by gas and abdominal fat. Moreover, ultrasound offers limited information regarding the lung parenchyma. On the other hand, high-resolution CT can be useful in identifying causes of diaphragmatic dysfunction, such as atrophy or eventration. However, it does not allow for the quantitative assessment of diaphragmatic movement and the differentiation between paralysis and dysfunction, especially in bilateral dysfunction, which is often overlooked due to the elevation of both hemidiaphragms. Dynamic Digital Radiography (DDR) has emerged as a valuable and innovative imaging technique due to its unique ability to evaluate diaphragm movement in real time, integrating dynamic functional information with static anatomical data. DDR provides both visual and quantitative analysis of the diaphragm's motion, including excursion and speed, which leads to a definitive diagnosis. Additionally, DDR offers a range of post-processing techniques that provide information on lung movement and pulmonary ventilation. Based on these findings, the patient was referred to a thoracic surgeon and deemed a candidate for surgical plication of the right diaphragm.

Lung auscultation using smartphone built-in microphone versus digital stethoscope: a comparative early feasibility study.

Lung auscultation using a smartphone built-in microphone is promising for home monitoring of pediatric respiratory diseases. Our aim was to compare respiratory sounds recorded by a smartphone and a digital stethoscope by assessing the proportion of quality recordings and adventitious sounds detected by each device. A comparative early feasibility study with children from a public school in Northern Portugal was conducted. Lung auscultation was performed at 7 locations (trachea, anterior superior, posterior superior, and posterior inferior regions of both hemithoraces). Two researchers recorded lung auscultation simultaneously with a smartphone and a digital stethoscope, and independently classified recordings regarding their quality (at least one respiratory cycle audible with minimal artefacts) and presence of adventitious sounds. A third researcher resolved the disagreements. Proportions of sounds with quality and with adventitious sounds were compared between devices using chi-square tests. Inter-device agreement was also assessed with proportion of agreement and Cohen kappa. Twenty-seven children (20 male, 10.2±0.6 years) were enrolled, 8 had self-reported asthma. A total of 378 respiratory sounds (smartphone N.=190, digital stethoscope N.=188 [2 missing]) were recorded. The percentage of recordings with quality was high in both devices (93% smartphone vs. 97% digital stethoscope, P=0.065). Adventitious sounds were identified in 11% and 19% of the recordings acquired with smartphone and digital stethoscope, respectively (P=0.021). The overall inter-device proportion of agreement for the presence of adventitious sounds was 90% (kappa 0.60, 95%CI 0.42, 0.77). Smartphone lung auscultation seems to be a viable technology for recording respiratory sounds and identifying adventitious sounds in children. Further research is needed to validate its potential for effective remote respiratory disease monitoring.