Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Fischer, Aurélie; Elbéji, Abir; Aguayo, Gloria; Fagherazzi, Guy

doi:10.2196/40655

Cited by 3 publications

(3 citation statements)

References 36 publications

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“…We further investigated whether digital biomarkers extracted from voice can act as a substitute for standard clinical measures estimated from questionnaires. Contrary to questionnaires which are mostly done during on-site clinical visits and can be tedious, voice recordings allow quick and easy-to-use data collection at patients' homes; thus, substantially facilitating remote patient monitoring [38]. Our vocal biomarkers outperformed socio-demographic/clinical predictive factors by approximately 1.5% in terms of accuracy, confirming their potential to be a surrogate for clinical measures.…”

Section: Discussionmentioning

confidence: 65%

Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice Study

Despotovic,

Elbéji,

Fünfgeld

et al. 2023

Preprint

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Regular monitoring of respiratory quality of life (RQoL) is essential in respiratory healthcare, facilitating prompt diagnosis and tailored treatment for chronic respiratory diseases. Voice alterations resulting from respiratory conditions create unique audio signatures that can potentially be utilized for disease screening or monitoring. Analyzing data from 1908 participants from the Colive Voice study, which collects standardized voice recordings alongside comprehensive demographic, epidemiological, and patient-reported outcome data, we evaluated various strategies to estimate RQoL from voice, including handcrafted acoustic features, standard acoustic feature sets, and advanced deep audio embeddings derived from pretrained convolutional neural networks. We compared models using clinical features alone, voice features alone, and a combination of both. The multimodal model combining clinical and voice features demonstrated the best performance, achieving an accuracy of 70.34% and an area under the receiver operating characteristic curve (AUROC) of 0.77; an improvement of 5% in terms of accuracy and 7% in terms of AUROC compared to model utilizing voice features alone. Incorporating vocal biomarkers significantly enhanced the predictive capacity of clinical variables across all acoustic feature types, with a net classification improvement (NRI) of up to 0.19. Our digital voice-based biomarker is capable of accurately predicting RQoL, either as an alternative to or in conjunction with clinical measures, and could be used to facilitate rapid screening and remote monitoring of respiratory health status.

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Section: Discussionmentioning

confidence: 65%

Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice Study

Despotovic,

Elbéji,

Fünfgeld

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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“…Additionally, the COVID-19 pandemic generated increased interest in developing technology to perform remote, minimally invasive respiratory disease diagnostics, and voicederived biomarkers have come to the forefront of this effort. [3][4][5][6][7] Many groups have also found grounds for the analysis of voice for the detection and progression of neurological disorders, such as Parkinson's, Alzheimer's, Huntington's, and dementia. [8][9][10][11][12][13][14][15][16] Additional areas of investigation include pediatric and adult voice disorders, and mental health disorders.…”

Section: Introductionmentioning

confidence: 99%

“…The vocal biomarkers market was worth USD 1901.1 million in 2021 and is expected to reach USD 5131.8 million in 2028 2 . Additionally, the COVID‐19 pandemic generated increased interest in developing technology to perform remote, minimally invasive respiratory disease diagnostics, and voice‐derived biomarkers have come to the forefront of this effort 3–7 . Many groups have also found grounds for the analysis of voice for the detection and progression of neurological disorders, such as Parkinson's, Alzheimer's, Huntington's, and dementia 8–16 .…”

Section: Introductionmentioning

confidence: 99%

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

Evangelista,

Kale,

McCutcheon

et al. 2023

The Laryngoscope

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IntroductionAccuracy and validity of voice AI algorithms rely on substantial quality voice data. Although commensurable amounts of voice data are captured daily in voice centers across North America, there is no standardized protocol for acoustic data management, which limits the usability of these datasets for voice artificial intelligence (AI) research.ObjectiveThe aim was to capture current practices of voice data collection, storage, analysis, and perceived limitations to collaborative voice research.MethodsA 30‐question online survey was developed with expert guidance from the voicecollab.ai members, an international collaborative of voice AI researchers. The survey was disseminated via REDCap to an estimated 200 practitioners at North American voice centers. Survey questions assessed respondents' current practices in terms of acoustic data collection, storage, and retrieval as well as limitations to collaborative voice research.ResultsSeventy‐two respondents completed the survey of which 81.7% were laryngologists and 18.3% were speech language pathologists (SLPs). Eighteen percent of respondents reported seeing 40%–60% and 55% reported seeing >60 patients with voice disorders weekly (conservative estimate of over 4000 patients/week). Only 28% of respondents reported utilizing standardized protocols for collection and storage of acoustic data. Although, 87% of respondents conduct voice research, only 38% of respondents report doing so on a multi‐institutional level. Perceived limitations to conducting collaborative voice research include lack of standardized methodology for collection (30%) and lack of human resources to prepare and label voice data adequately (55%).ConclusionTo conduct large‐scale multi‐institutional voice research with AI, there is a pertinent need for standardization of acoustic data management, as well as an infrastructure for secure and efficient data sharing.Level of EvidenceLevel 5 Laryngoscope, 2023

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Co-Design of a Voice-Based Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 (UpcomingVoice Study): Protocol for a Mixed Methods Study

Fischer¹,

Aguayo²,

Oustric³

et al. 2023

JMIR Res Protoc

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Background Between 10% and 20% of people with a COVID-19 infection will develop the so-called long COVID syndrome, which is characterized by fluctuating symptoms. Long COVID has a high impact on the quality of life of affected people, who often feel abandoned by the health care system and are demanding new tools to help them manage their symptoms. New digital monitoring solutions could allow them to visualize the evolution of their symptoms and could be tools to communicate with health care professionals (HCPs). The use of voice and vocal biomarkers could facilitate the accurate and objective monitoring of persisting and fluctuating symptoms. However, to assess the needs and ensure acceptance of this innovative approach by its potential users—people with persisting COVID-19–related symptoms, with or without a long COVID diagnosis, and HCPs involved in long COVID care—it is crucial to include them in the entire development process. Objective In the UpcomingVoice study, we aimed to define the most relevant aspects of daily life that people with long COVID would like to be improved, assess how the use of voice and vocal biomarkers could be a potential solution to help them, and determine the general specifications and specific items of a digital health solution to monitor long COVID symptoms using vocal biomarkers with its end users. Methods UpcomingVoice is a cross-sectional mixed methods study and consists of a quantitative web-based survey followed by a qualitative phase based on semistructured individual interviews and focus groups. People with long COVID and HCPs in charge of patients with long COVID will be invited to participate in this fully web-based study. The quantitative data collected from the survey will be analyzed using descriptive statistics. Qualitative data from the individual interviews and the focus groups will be transcribed and analyzed using a thematic analysis approach. Results The study was approved by the National Research Ethics Committee of Luxembourg (number 202208/04) in August 2022 and started in October 2022 with the launch of the web-based survey. Data collection will be completed in September 2023, and the results will be published in 2024. Conclusions This mixed methods study will identify the needs of people affected by long COVID in their daily lives and describe the main symptoms or problems that would need to be monitored and improved. We will determine how using voice and vocal biomarkers could meet these needs and codevelop a tailored voice-based digital health solution with its future end users. This project will contribute to improving the quality of life and care of people with long COVID. The potential transferability to other diseases will be explored, which will contribute to the deployment of vocal biomarkers in general. Trial Registration ClinicalTrials.gov NCT05546918; https://clinicaltrials.gov/ct2/show/NCT05546918 International Registered Report Identifier (IRRID) DERR1-10.2196/46103

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Recommendations for Successful Implementation of the Use of Vocal Biomarkers for Remote Monitoring of COVID-19 and Long COVID in Clinical Practice and Research

Cited by 3 publications

References 36 publications

Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice Study

Digital Voice-Based Biomarker for Monitoring Respiratory Quality of Life: Findings from the Colive Voice Study

Current Practices in Voice Data Collection and Limitations to Voice AI Research: A National Survey

Co-Design of a Voice-Based Digital Health Solution to Monitor Persisting Symptoms Related to COVID-19 (UpcomingVoice Study): Protocol for a Mixed Methods Study

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