Quality assessment and feedback of Smart Device Microphone Spirometry executed by children

Almeida, Rute; Pinho, Bernardo; Jácome, Cristina; Teixeira, João F.; Amaral, Rita; Lopes, Filipa; Jacinto, Tiago; Guedes, Rui; Pereira, Mariana; Gonçalves, Ivânia; Fonseca, João Almeida

doi:10.1109/enbeng.2019.8692452

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…The embedded microphone will be used to acquire the sounds of forced expiratory maneuvers (microphone spirometry) and computerized lung auscultation. The processing of the sound includes automatic quality check in particular regarding the correct performance of the maneuver, as well as extraction of features with potential clinical value (Fonseca et al, 2015;Pinho et al, 2018;Almeida et al, 2019Almeida et al, , 2020. These sounds can also be made available to the healthcare professional for further analysis.…”

Section: Self-monitoring Modulementioning

confidence: 99%

Airdoc: Smart Mobile Application for Individualized Support and Monitoring of Respiratory Function and Sounds of Patients With Chronic Obstructive Disease

2020

Proceedings of the 12th International Conference on E-Health (EH2020)

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Current tools for self-management of chronic obstructive respiratory diseases (CORD) are difficult to use, not individualized and requiring laborious analysis by health professionals, discouraging their use in healthcare. There is an opportunity for cost-effective and easy-to-disseminate advanced technological solutions directed to patients and attractive to different stakeholders. The strategy of AIRDOC is to develop and integrate self-monitoring and self-managing tools, making use of the smartphone's presence in everyday life. AIRDOC intends to innovate on: i) technologies for remote monitoring of respiratory function and computerized lung auscultation; ii) coaching solutions, integrating psychoeducation, gamification and disease management support systems; and iii) management of personal health data, focusing on security, privacy and interoperability. It is expected that AIRDOC results will contribute for the innovation in CORD healthcare, with increased patient involvement and empowerment while providing quality prospective information for better clinical decisions, allowing more efficient and sustainable healthcare delivery.

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Section: Self-monitoring Modulementioning

confidence: 99%

Airdoc: Smart Mobile Application for Individualized Support and Monitoring of Respiratory Function and Sounds of Patients With Chronic Obstructive Disease

2020

Proceedings of the 12th International Conference on E-Health (EH2020)

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“…A separate field of recent developments is spirometry quality assessment in the so-called sound spirometry, i.e. spirometry without the supervision of a physician where a diagnostic signal is sound recorded through the mobile phone (Pinho et al 2018, Viswanath et al 2018, Almeida et al 2020, Rahman et al 2020 or tablet microphone (Almeida et al 2019). Smartphone spirometry is particularly susceptible to poorly performed efforts because of environmental sounds or mistakes in the effort.…”

Section: Introductionmentioning

confidence: 99%

Deep learning algorithm for visual quality assessment of the spirograms

et al. 2023

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The quality of spirometry manoeuvres is crucial for correctly interpreting the values of spirometry parameters. A fundamental guideline for proper quality assessment is the ATS/ERS Standards for spirometry, updated in 2019, which describe several start-of-test and end-of-test criteria which can be assessed automatically. However, the spirometry standards also require a visual evaluation of the spirometry curve to determine the spirograms’ acceptability or usability. In this study, we present an automatic algorithm based on a convolutional neural network (CNN) for quality assessment of the spirometry curves as an alternative to manual verification performed by specialists. The algorithm for automatic assessment of spirometry measurements was created using a set of randomly selected 1998 spirograms which met all quantitative criteria defined by ATS/ERS Standards. Each spirogram was annotated as 'confirm' (remaining acceptable or usable status) or 'reject' (change the status to unacceptable) by four pulmonologists, separately for FEV1 and FVC parameters. The database was split into a training (80%) and test set (20%) for developing the CNN classification algorithm. The algorithm was optimised using a cross-validation method. The accuracy, sensitivity and specificity obtained for the algorithm were 92.3%, 92.8% and 90.0% for FEV1 and 92.6%, 93.4% and 89.6% for FVC, respectively. The algorithm provides an opportunity to significantly improve the quality of spirometry tests, especially during unsupervised spirometry. It can also serve as an additional tool in clinical trials to quickly assess the quality of a large group of tests.

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Automatic Quality Assessment of a Forced Expiratory Manoeuvre Acquired with the Tablet Microphone

et al. 2019

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Quality assessment and feedback of Smart Device Microphone Spirometry executed by children

Cited by 3 publications

References 18 publications

Airdoc: Smart Mobile Application for Individualized Support and Monitoring of Respiratory Function and Sounds of Patients With Chronic Obstructive Disease

Airdoc: Smart Mobile Application for Individualized Support and Monitoring of Respiratory Function and Sounds of Patients With Chronic Obstructive Disease

Deep learning algorithm for visual quality assessment of the spirograms

Automatic Quality Assessment of a Forced Expiratory Manoeuvre Acquired with the Tablet Microphone

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