Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

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Idrisoglu, AlperBlekinge Tekniska Högskola,Institutionen för hälsa (author)

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders : Systematic Literature Review

Article/chapterEnglish2023

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JMIR Publications,2023
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:his-23072
https://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23072URI
https://doi.org/10.2196/46105DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:bth-25253URI

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Language:English
Summary in:English

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SwePubSwePub

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Subject category:ref swepub-contenttype
Subject category:for swepub-publicationtype

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CC BY 4.0©Alper Idrisoglu, Ana Luiza Dallora, Peter Anderberg, Johan Sanmartin Berglund. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.07.2023.Corresponding Author: Alper Idrisoglu, MSc, Department of Health, Blekinge Institute of Technology, Valhallavägen 1, Karlskrona, 37141 SwedenPhone: 46 701462619 Email: alper.idrisoglu@bth.seThe authors thank the Excellence Center at Linköping – Lund in Information Technology (ELLIIT) for funding and supporting this project.
BACKGROUND: Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. OBJECTIVE: This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. METHODS: This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. RESULTS: In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. CONCLUSIONS: This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Annan medicin och hälsovetenskap Övrig annan medicin och hälsovetenskap hsv//swe
MEDICAL AND HEALTH SCIENCES Other Medical and Health Sciences Other Medical and Health Sciences not elsewhere specified hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
diagnosis
digital biomarkers
machine learning
monitoring
voice features
voice-affecting disorder
Humans
Monitoring
Physiologic
human
physiologic monitoring
Family-Centred Health
Familjecentrerad hälsa (FamCeH)

Added entries (persons, corporate bodies, meetings, titles ...)

Moraes, Ana Luiza DalloraBlekinge Tekniska Högskola,Institutionen för hälsa(Swepub:bth)ada (author)
Anderberg, Peter,Professor,1963-Blekinge Tekniska Högskola,Högskolan i Skövde,Institutionen för hälsovetenskaper,Forskningsmiljön hälsa, hållbarhet och digitalisering,Department of Health, Blekinge Institute of Technology, Karlskrona, Sweden,Familjecentrerad hälsa (FamCeH), Family-Centred Health,Institutionen för hälsa(Swepub:bth)pan (author)
Sanmartin Berglund, Johan,ProfessorBlekinge Tekniska Högskola,Institutionen för hälsa(Swepub:bth)jbu (author)
Blekinge Tekniska HögskolaInstitutionen för hälsa (creator_code:org_t)

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