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

Voice as a Biomarker in Health-Tech: Mapping the Evolving Landscape of Voice Biomarkers in the Start-Up World

OBJECTIVE

The vocal biomarkers market was worth $1.9B in 2021 and is projected to exceed $5.1B by 2028, for a compound annual growth rate of 15.15%. The investment growth demonstrates a blossoming interest in voice and artificial intelligence (AI) as it relates to human health. The objective of this study was to map the current landscape of start-ups utilizing voice as a biomarker in health-tech.

DATA SOURCES

A comprehensive search for start-ups was conducted using Google, LinkedIn, Twitter, and Facebook. A review of the research was performed using company website, PubMed, and Google Scholar.

REVIEW METHODS

A 3-pronged approach was taken to thoroughly map the landscape. First, an internet search was conducted to identify current start-ups focusing on products relating to voice as a biomarker of health. Second, Crunchbase was utilized to collect financial and organizational information. Third, a review of the literature was conducted to analyze publications associated with the identified start-ups.

RESULTS

A total of 27 start-up start-ups with a focus in the utilization of AI for developing biomarkers of health from the human voice were identified. Twenty-four of these start-ups garnered $178,808,039 in investments. The 27 start-ups published 194 publications combined, 128 (66%) of which were peer reviewed.

CONCLUSION

There is growing enthusiasm surrounding voice as a biomarker in health-tech. Academic drive may complement commercialization to best achieve progress in this arena. More research is needed to accurately capture the entirety of the field, including larger industry players, academic institutions, and non-English content.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania

BACKGROUND

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.

OBJECTIVE

This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.

METHODS

This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values.

RESULTS

This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.

CONCLUSIONS

Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54388.

Definitions of digital biomarkers: a systematic mapping of the biomedical literature

BACKGROUND

Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research.

METHODS

We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses.

RESULTS

We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions.

CONCLUSIONS

The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

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.

Detecting acute respiratory diseases in the pediatric population using cough sound features and machine learning: A systematic review

BACKGROUND

Acute respiratory diseases are a leading cause of morbidity and mortality in children. Cough is a common symptom of acute respiratory diseases and the sound of cough can be indicative of the respiratory disease. However, cough sound assessment in routine clinical practice is limited to human perception and the skills of the clinician. Objective cough sound evaluation has the potential to aid clinicians in acute respiratory disease diagnosis. In this systematic review, we assess and summarize the predictive ability of machine learning algorithms in analyzing cough sounds of acute respiratory diseases in the pediatric population.

METHOD

Our systematic search of the Scopus, Medline, and Embase databases on 25 January 2023 identified six articles meeting the inclusion criteria. Quality assessment of the included studies was performed using the checklist for the assessment of medical artificial intelligence.

RESULTS

Our analysis shows variability in the input to the machine learning algorithms, such as the use of various cough sound features and combining cough sound features with clinical features. The use of the machine learning algorithms also varies from conventional algorithms, such as logistic regression and support vector machine, to deep learning techniques, such as convolutional neural networks. The classification accuracy for the detection of bronchiolitis, croup, pertussis, and pneumonia across five articles is in the range of 82-96%. However, a significant drop is observed in the detection accuracy for bronchiolitis and pneumonia in the remaining article.

CONCLUSION

The number of articles is limited but, in general, the predictive ability of cough sound classification algorithms in childhood acute respiratory diseases shows promise.

Drug resistant tuberculosis: Implications for transmission, diagnosis, and disease management

Drug resistant tuberculosis contributes significantly to the global burden of antimicrobial resistance, often consuming a large proportion of the healthcare budget and associated resources in many endemic countries. The rapid emergence of resistance to newer tuberculosis therapies signals the need to ensure appropriate antibiotic stewardship, together with a concerted drive to develop new regimens that are active against currently circulating drug resistant strains. Herein, we highlight that the current burden of drug resistant tuberculosis is driven by a combination of ongoing transmission and the intra-patient evolution of resistance through several mechanisms. Global control of tuberculosis will require interventions that effectively address these and related aspects. Interrupting tuberculosis transmission is dependent on the availability of novel rapid diagnostics which provide accurate results, as near-patient as is possible, together with appropriate linkage to care. Contact tracing, longitudinal follow-up for symptoms and active mapping of social contacts are essential elements to curb further community-wide spread of drug resistant strains. Appropriate prophylaxis for contacts of drug resistant index cases is imperative to limit disease progression and subsequent transmission. Preventing the evolution of drug resistant strains will require the development of shorter regimens that rapidly eliminate all populations of mycobacteria, whilst concurrently limiting bacterial metabolic processes that drive drug tolerance, mutagenesis and the ultimate emergence of resistance. Drug discovery programs that specifically target bacterial genetic determinants associated with these processes will be paramount to tuberculosis eradication. In addition, the development of appropriate clinical endpoints that quantify drug tolerant organisms in sputum, such as differentially culturable/detectable tubercle bacteria is necessary to accurately assess the potential of new therapies to effectively shorten treatment duration. When combined, this holistic approach to addressing the critical problems associated with drug resistance will support delivery of quality care to patients suffering from tuberculosis and bolster efforts to eradicate this disease.

Making cough count in tuberculosis care

Cough assessment is central to the clinical management of respiratory diseases, including tuberculosis (TB), but strategies to objectively and unobtrusively measure cough are lacking. Acoustic epidemiology is an emerging field that uses technology to detect cough sounds and analyze cough patterns to improve health outcomes among people with respiratory conditions linked to cough. This field is increasingly exploring the potential of artificial intelligence (AI) for more advanced applications, such as analyzing cough sounds as a biomarker for disease screening. While much of the data are preliminary, objective cough assessment could potentially transform disease control programs, including TB, and support individual patient management. Here, we present an overview of recent advances in this field and describe how cough assessment, if validated, could support public health programs at various stages of the TB care cascade.

Zimmer et al. discuss the importance of cough assessment in clinical management of tuberculosis (TB). They describe how acoustic epidemiology, which uses recording devices and artificial intelligence to detect, record and analyze cough, can be used in TB control and individual patient management.

[Mobile applications (apps) for diagnosis and treatment control in pediatric and adolescent medicine]

Die Digitalisierung hält in der Medizin in vielfältigster Form Einzug. Ob patientenzentriert, vernetzend, zur Unterstützung medizinischen Fachpersonals oder in der (klinischen) Forschung: Digitale Technologien sind aus dem medizinischen Alltag spätestens seit der durch das SARS-CoV-2 Virus ausgelösten Pandemie nicht mehr wegzudenken. Hierbei zählen u. a. mobile Smartphone-Anwendungen zu den häufigsten Entwicklungen. Doch die Vielzahl der erhältlichen Produkte und der Zeitmangel in der medizinischen Praxis machen eine zuverlässige Einschätzung der Qualität, Sicherheit und Funktionalität oft schwer. Der vorliegende Übersichtsbeitrag fasst aktuelle Entwicklungen „mobiler“ Technologien aus dem Bereich der Kinder- und Jugendmedizin zusammen und veranschaulicht erhältliche Anwendungen anhand konkreter Beispiele. Ziel ist es, die Leser:innen zu animieren, eigene Erfahrungen zu machen und ihren Blick für evtl. Risiken zu schärfen.

A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images

Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone app is conceptualised as a mass-deployable screening tool, which could alter the course of this pandemic. This app’s deployment could minimise the number of people accessing the limited and expensive confirmatory tests, thereby reducing the burden on the severely stressed healthcare infrastructure.

End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19: A Theoretical Framework

Respiratory symptoms can be caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms: coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases, such as the COVID-19 pandemic. Among the factors that contributed to the spread of the COVID-19 pandemic were the late diagnosis or misinterpretation of COVID-19 symptoms as regular flu-like symptoms. Research has shown that one of the possible differentiators of the underlying causes of different respiratory diseases could be the cough sound, which comes in different types and forms. A reliable lab-free tool for early and accurate diagnosis, which can differentiate between different respiratory diseases is therefore very much needed, particularly during the current pandemic. This concept paper discusses a medical hypothesis of an end-to-end portable system that can record data from patients with symptoms, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly theoretical solution could play an important part in the early diagnosis.