Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study

Audio-based digital biomarkers in diagnosing and managing respiratory diseases: a systematic review and bibliometric analysis

Advances in wearable sensors and artificial intelligence have greatly enhanced the potential of digitised audio biomarkers for disease diagnostics and monitoring. In respiratory care, evidence supporting their clinical use remains fragmented and inconclusive. This study aimed to assess the current research landscape of digital audio biomarkers in respiratory medicine through a bibliometric analysis and systematic review (PROSPERO CRD 42022336730). MEDLINE, Embase, Cochrane Library and CINAHL were searched for references indexed up to 9 April 2024. Eligible studies evaluated the accuracy of sound analysis for diagnosing and managing obstructive (asthma and COPD) or infectious respiratory diseases, excluding COVID-19. A narrative synthesis was conducted, and the QUADAS-2 tool was used to assess study quality and risk of bias. Of 14 180 studies, 81 were included. Bibliometric analysis identified fundamental (e.g. "diagnostic accuracy"+"machine learning") and emerging (e.g. "developing countries") themes. Despite methodological heterogeneity, audio biomarkers generally achieved moderate (60-79%) to high (80-100%) accuracies. 80% of studies (eight out of ten) reported high sensitivities and specificities for asthma diagnosis, 78% (seven out of nine) reported high sensitivities and 56% (five out of nine) reported high specificities for COPD, and 64% (seven out of eleven) reported high sensitivity or specificity values for pneumonia diagnosis. Breathing and coughing were the most common biomarkers, with artificial neural networks being the most common analysis technique. Future research on audio biomarkers should focus on testing their validity in clinically diverse populations and resolving algorithmic bias. If successful, digital audio biomarkers hold promise for complementing existing clinical tools in enabling more accessible applications in telemedicine, communicable disease monitoring, and chronic condition management.

Intelligent wearable devices with audio collection capabilities to assess chronic obstructive pulmonary disease severity

Background

Intelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.

Objective

The objective was to assess COPD severity via physiological parameters and audio data collected by a smartwatch.

Methods

COPD patients underwent lung function tests, electrocardiograms, blood gas analysis, and 6-min walk tests. The patients' peripheral arterial oxygen saturation (SpO2), heart rate variability (HRV), heart rate (HR), and respiratory rate (RR) were continuously monitored via a smartwatch for 7-14 days, and voluntary cough and forceful blowing sounds were recorded twice daily. The HR, SpO2, and RR were categorized into all-day, sleep, and wake periods and summarized using the mean, standard deviation, median, 25th percentile, 75th percentile and percent variation. The correlations among lung function, physiological parameters, and audio data were analyzed to develop a model for predicting COPD severity.

Results

Twenty-nine stable patients, with a mean age of 67.0 ± 5.8 years, were enrolled, and 89.7% were male. HR, HRV, RR, cough sounds, and blowing sounds were significantly correlated with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade, with cough sounds showing the highest correlation (r = 0.7617, p < .001). Cough sounds also had the strongest correlation with the mean 6-minute walking distance (r = 0.6847, p < .001), whereas blowing sounds had the strongest correlation with the Body mass index, airflow Obstruction, Dyspnea, and Exercise capacity index (r = -0.6749, p < .001). A logistic regression model using the RR and blowing sounds as key predictors achieved accuracies of 0.77-0.89 in determining the GOLD grade, with a Cohen's kappa coefficient of 0.6757.

Conclusions

Audio data were more strongly correlated with lung function in COPD patients than were physiological parameters. A smartwatch with audio collection capabilities effectively assessed COPD severity.

Trial Registration

ClinicalTrials.gov NCT05551169.

Identifying unique spectral fingerprints in cough sounds for diagnosing respiratory ailments

Coughing, a prevalent symptom of many illnesses, including COVID-19, has led researchers to explore the potential of cough sound signals for cost-effective disease diagnosis. Traditional diagnostic methods, which can be expensive and require specialized personnel, contrast with the more accessible smartphone analysis of coughs. Typically, coughs are classified as wet or dry based on their phase duration. However, the utilization of acoustic analysis for diagnostic purposes is not widespread. Our study examined cough sounds from 1183 COVID-19-positive patients and compared them with 341 non-COVID-19 cough samples, as well as analyzing distinctions between pneumonia and asthma-related coughs. After rigorous optimization across frequency ranges, specific frequency bands were found to correlate with each respiratory ailment. Statistical separability tests validated these findings, and machine learning algorithms, including linear discriminant analysis and k-nearest neighbors classifiers, were employed to confirm the presence of distinct frequency bands in the cough signal power spectrum associated with particular diseases. The identification of these acoustic signatures in cough sounds holds the potential to transform the classification and diagnosis of respiratory diseases, offering an affordable and widely accessible healthcare tool.

Effect of wheeze and lung function on lung sound parameters in children with asthma

BACKGROUND

In children with asthma, there are many cases in which wheeze is confirmed by auscultation with a normal lung function, or in which the lung function is decreased without wheeze. Using an objective lung sound analysis, we examined the effect of wheeze and the lung function on lung sound parameters in children with asthma.

METHODS

A total of 114 children with asthma (males to females = 80: 34, median age 10 years old) were analyzed for their lung sound parameters using conventional methods, and wheeze and the lung function were checked. The effects of wheeze and the lung function on lung sound parameters were examined.

RESULTS

The patients with wheeze or decreased forced expiratory flow and volume in 1 s (FEV1) (% pred) showed a significantly higher sound power of respiration and expiration-to-inspiration sound power ratio (E/I) than those without wheeze and a normal FEV1 (% pred). There was no marked difference in the sound power of respiration or E/I between the patients without wheeze and a decreased FEV1 (% pred) and the patients with wheeze and a normal FEV1 (% pred).

CONCLUSIONS

Our data suggest that bronchial constriction in the asthmatic children with wheeze similarly exists in the asthmatic children with a decreased lung function. A lung sound analysis is likely to enable an accurate understanding of airway conditions.

A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm's positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm's performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Chronic cough-the limitation and advances in assessment techniques

Accurate and consistent assessments of cough are essential to advance the understanding of the mechanisms of cough and individualised the management of patients. Considerable progress has been made in this work. Here we reviewed the currently available tools for subjectively and objectively measuring both cough sensitivity and severity. We also provided some opinions on the new techniques and future directions. The simple and practical Visual Analogue Scale (VAS), the Leicester Cough Questionnaire (LCQ), and the Cough Specific Quality of Life Questionnaire (CQLQ) are the most widely used self-reported questionnaires for evaluating and quantifying cough severity. The Hull Airway Reflux Questionnaire (HARQ) is a tool to elucidate the constellation of symptoms underlying the diagnosis of chronic cough. Chemical excitation tests are widely used to explore the pathophysiological mechanisms of the cough reflex, such as capsaicin, citric acid and adenosine triphosphate (ATP) challenge test. Cough frequency is an ideal primary endpoint for clinical research, but the application of cough counters has been limited in clinical practice by the high cost and reliance on aural validation. The ongoing development of cough detection technology for smartphone apps and wearable devices will hopefully simplify cough counting, thus transitioning it from niche research to a widely available clinical application.

Smartphone in medicine — from a reference book to a diagnostic system. Overview of the current state of the issue

The paper provides a brief overview of the modern possibilities of using a smartphone as a diagnostic device of a wide profile. In some cases, additional specialized attachments are required. In others, the diagnostic algorithm uses only standard cameras, a microphone and various built-in smartphone sensors. The development of the smartphone integration into the healthcare system is modern, relevant and very promising, given the widespread use of smartphones among the global population.

Effect Analysis of Lung Rehabilitation Training in 5A Nursing Mode for Elderly Patients with COPD Based on X-Ray

This study was aimed at evaluating the application effect of pulmonary rehabilitation training under 5A nursing mode based on X-ray in elderly patients with chronic obstructive pulmonary disease (COPD). Then, 84 elderly patients with chronic obstructive emphysema were selected as the research subjects. COPD knowledge level questionnaire, caregiver self-efficacy scale (CSES), COPD assessment test (CAT), and 6-minute walking experiment (6MWD) were adopted, and the clinical application effect of pulmonary rehabilitation training and conventional nursing under 5A nursing mode was comprehensively compared. The results show that after two and four months of intervention, the average score of COPD knowledge level questionnaire in the test group was 27.43 points and 30.08 points, respectively, higher than that in the control group (P < 0.05). After two and four months of intervention, the number of patients with good compliance in the test group was remarkably improved, and the severity of airflow restriction in the test group was slower than that in the control group. In short, pulmonary rehabilitation training under 5A nursing mode based on X-ray can effectively improve the disease knowledge level, self-efficacy, and pulmonary rehabilitation training compliance of elderly COPD patients, which played an important role in improving the quality of life of patients and alleviating the degree of dyspnea of patients.

Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review

Cough is a very common symptom and the most frequent reason for seeking medical advice. Optimized care goes inevitably through an adapted recording of this symptom and automatic processing. This study provides an updated exhaustive quantitative review of the field of cough sound acquisition, automatic detection in longer audio sequences and automatic classification of the nature or disease. Related studies were analyzed and metrics extracted and processed to create a quantitative characterization of the state-of-the-art and trends. A list of objective criteria was established to select a subset of the most complete detection studies in the perspective of deployment in clinical practice. One hundred and forty-four studies were short-listed, and a picture of the state-of-the-art technology is drawn. The trend shows an increasing number of classification studies, an increase of the dataset size, in part from crowdsourcing, a rapid increase of COVID-19 studies, the prevalence of smartphones and wearable sensors for the acquisition, and a rapid expansion of deep learning. Finally, a subset of 12 detection studies is identified as the most complete ones. An unequaled quantitative overview is presented. The field shows a remarkable dynamic, boosted by the research on COVID-19 diagnosis, and a perfect adaptation to mobile health.

Remote Monitoring for Prediction and Management of Acute Exacerbations in Chronic Obstructive Pulmonary Disease (AECOPD)

The progression of chronic obstructive pulmonary disease (COPD) is characterized by episodes of acute exacerbation (AECOPD) of symptoms, decline in respiratory function, and reduction in quality-of-life increasing morbi-mortality and often requiring hospitalization. Exacerbations can be triggered by environmental exposures, changes in lifestyle, and/or physiological and psychological factors to greater or lesser extents depending on the individual’s COPD phenotype. The prediction and early detection of an exacerbation might allow patients and physicians to better manage the acute phase. We summarize the recent scientific data on remote telemonitoring (TM) for the prediction and management of acute exacerbations in COPD patients. We discuss the components of remote monitoring platforms, including the integration of environmental monitoring data; patient reported outcomes collected via interactive Smartphone apps, with data from wearable devices that monitor physical activity, heart rate, etc.; and data from medical devices such as connected non-invasive ventilators. We consider how telemonitoring and the deluge of data it potentially generates could be combined with electronic health records to provide personalized care and multi-disease management for COPD patients.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments

Background: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. Objective: To evaluate the diagnostic accuracy of emergency department clinicians in diagnosing acute childhood respiratory diseases, as compared with expert panel consensus (reference standard). Methods: Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. Results: For 620 participants, the sensitivity and specificity (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (64.9 [54.6, 74.4], 91.0 [88.2, 93.3]), croup (76.8 [66.2, 85.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.6 [83.1, 89.6], 92.9 [87.6, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (63·9 [50.6, 75·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9 and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. Conclusion: In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.

Lung sound analysis in infants with risk factors for asthma development

BACKGROUND AND OBJECTIVES

Using a lung sound analysis, the prognosis of asthma was investigated in infants with risk factors for asthma development by a long-term observation.

METHODS

A total of 268 infants were included (median age: 8 months old). The lung sound parameters (the ratio of the third and fourth area to the total area under the curve [A3/AT and B4/AT], and the ratio of power and frequency at 50% and 75% of the highest frequency [RPF50 and RPF75]) were evaluated at the first visit. At 3 years old, using a questionnaire, we examined the relationship between the lung sound parameters and risk factors of asthma development.

RESULTS

Among the 268 infants, 175 infants were in good health and 93 had a history of acute respiratory infection (ARI) within 7 days at the first visit. Among the 3- to 12-month-old infants with an ARI, the A3/AT, B4/AT values in those with a history of asthma/asthmatic bronchitis, atopic dermatitis, and atopy were smaller than in the infants without such histories. Among the 13- to 24-month-old infants with an ARI, the A3/AT and B4/AT values in those with a wheezing history were larger than in the infants without such a history.

CONCLUSIONS

The characteristics of the lung sounds in infants with risk factors for asthma development were demonstrated over long-term follow-up. Lung sound analyses may be useful for assessing the airway condition of infants.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9–89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4–96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0–87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

The diagnosis of respiratory disease in children using a phone-based cough and symptom analysis algorithm: The smartphone recordings of cough sounds 2 (SMARTCOUGH-C 2) trial design

The diagnosis of acute respiratory diseases in children can be challenging, and no single objective diagnostic test exists for common pediatric respiratory diseases. Previous research has demonstrated that ResAppDx, a cough sound and symptom-based analysis algorithm, can identify common respiratory diseases at the point of care. We present the study protocol for SMARTCOUGH-C 2, a prospective diagnostic accuracy trial of a cough and symptom-based algorithm in a cohort of children presenting with acute respiratory diseases. The objective of the study is to assess the performance characteristics of the ResAppDx algorithm in the diagnosis of common pediatric acute respiratory diseases.