Automatic Croup Diagnosis Using Cough Sound Recognition

Sharan, Roneel V.; Abeyratne, Udantha R.; Swarnkar, Vinayak; Porter, Paul

doi:10.1109/tbme.2018.2849502

Cited by 85 publications

(57 citation statements)

References 15 publications

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“…Sharan et al [85] proposes using gammatone filter models which mimic the human auditory system. The gammatone filter is augmented with linear MFCCs for extracting the frequency components of cough sound sample.…”

Section: Ml-enabled Workflow For Cough Analysismentioning

confidence: 99%

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

et al. 2021

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The COVID-19 pandemic has overwhelmed the existing healthcare infrastructure in many parts of the world. Healthcare professionals are not only over-burdened but also at a high risk of nosocomial transmission from COVID-19 patients. Screening and monitoring the health of a large number of susceptible or infected individuals is a challenging task. Although professional medical attention and hospitalization are necessary for high-risk COVID-19 patients, home isolation is an effective strategy for low and medium risk patients as well as for those who are at risk of infection and have been quarantined. However, this necessitates effective techniques for remotely monitoring the patients’ symptoms. Recent advances in Machine Learning (ML) and Deep Learning (DL) have strengthened the power of imaging techniques and can be used to remotely perform several tasks that previously required the physical presence of a medical professional. In this work, we study the prospects of vital signs monitoring for COVID-19 infected as well as quarantined individuals by using DL and image/signal-processing techniques, many of which can be deployed using simple cameras and sensors available on a smartphone or a personal computer, without the need of specialized equipment. We demonstrate the potential of ML-enabled workflows for several vital signs such as heart and respiratory rates, cough, blood pressure, and oxygen saturation. We also discuss the challenges involved in implementing ML-enabled techniques.

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Section: Ml-enabled Workflow For Cough Analysismentioning

confidence: 99%

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

et al. 2021

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“…Between Jan 2016 and Nov 2017, we recruited subjects to train and refine the algorithm. The mathematical techniques used to derive our algorithms have been described elsewhere (16)(17)(18)20). Briefly, selected cough audio-segments and patient-reported symptoms were extracted from the training cohort and combined into several continuous classifier models to determine the probability of CAP.…”

Section: Development Of the Algorithm (Index Test)mentioning

confidence: 99%

“…In 2015 we commenced a digital diagnostic program to develop algorithms that combine a mathematical analysis of cough-associated audio segments with patient-reported symptoms to identify respiratory diseases in children and adults (16)(17)(18). The forced expiratory air column produced during a cough supports a higher bandwidth than that across the chest wall which is relied upon in traditional auscultation.…”

Section: Introductionmentioning

confidence: 99%

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations

et al. 2020

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Background: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiologic examinations are not possible, such as during telehealth consultations. Aim: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiology inputs. Design and Setting: A prospective cohort study using data from subjects aged over 12 years presenting with acute respiratory symptoms to a hospital in Western Australia. Method: Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP. We recruited independent cohorts to train and test the accuracy of the algorithm. Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians. Specialist radiologists reported medical imaging. Results: The algorithm had high percent agreement (PA) with the clinical diagnosis of CAP in the total cohort (n=322, Positive PA=86%, Negative PA=86%, AUC=0.95); in subjects 22-65 years (n=192, PPA=86%, NPA=87%, AUC=0.94) and in subjects >65 years (n=86, PPA=86%, NPA=87.5%, AUC=0.94). Agreement was preserved across CAP severity: 85% (80/94) of subjects with CRB-65 scores 1-2, and 87% (57/65) with a score of 0, were correctly diagnosed by the algorithm. Conclusion: The algorithm provides rapid and accurate diagnosis of CAP. It offers improved accuracy over current protocols when clinical evaluation is difficult. It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.

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“…With pilot studies demonstrating a sensitivity of 94% and specificity 88% for differentiating pneumonia from no disease, this method has been shown to outperform the World Health Organization (WHO) clinical algorithm for pneumonia diagnosis in resource-poor regions [18]. Subsequently, we have described similar technology for the diagnosis of croup, the most common cause of upper airway obstruction in children between 6 months and 6 years; [19, 20] and reported on the tool’s ability to predict spirometry readings in adults with chronic lung disease [21]. In these studies, automated cough sound analysis required minimal operator training, and was able to provide robust diagnostic accuracies for the specified conditions without the need for clinical auscultation or diagnostic support testing.…”

Section: Introductionmentioning

confidence: 99%

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children

et al. 2019

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Background The differential diagnosis of paediatric respiratory conditions is difficult and suboptimal. Existing diagnostic algorithms are associated with significant error rates, resulting in misdiagnoses, inappropriate use of antibiotics and unacceptable morbidity and mortality. Recent advances in acoustic engineering and artificial intelligence have shown promise in the identification of respiratory conditions based on sound analysis, reducing dependence on diagnostic support services and clinical expertise. We present the results of a diagnostic accuracy study for paediatric respiratory disease using an automated cough-sound analyser. Methods We recorded cough sounds in typical clinical environments and the first five coughs were used in analyses. Analyses were performed using cough data and up to five-symptom input derived from patient/parent-reported history. Comparison was made between the automated cough analyser diagnoses and consensus clinical diagnoses reached by a panel of paediatricians after review of hospital charts and all available investigations. Results A total of 585 subjects aged 29 days to 12 years were included for analysis. The Positive Percent and Negative Percent Agreement values between the automated analyser and the clinical reference were as follows: asthma (97, 91%); pneumonia (87, 85%); lower respiratory tract disease (83, 82%); croup (85, 82%); bronchiolitis (84, 81%). Conclusion: The results indicate that this technology has a role as a high-level diagnostic aid in the assessment of common childhood respiratory disorders. Trial registration Australian and New Zealand Clinical Trial Registry (retrospective) - ACTRN12618001521213 : 11.09.2018.

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Automatic Croup Diagnosis Using Cough Sound Recognition

Abstract: This work has the potential to automate croup diagnosis based solely on cough sound analysis.

Cited by 85 publications

References 15 publications

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

AI-enabled remote monitoring of vital signs for COVID-19: methods, prospects and challenges

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations

A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children

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