Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 30, 2023
Date Accepted: May 23, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Applied machine learning techniques to diagnose voice-affecting conditions and disorders: A systematic literature review
ABSTRACT
Background:
Normal voice production depends on the synchronized cooperation of multiple biological systems, which makes the voice sensitive to changes. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Furthermore, emerging Machine Learning (ML) technologies have enabled it to extract 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 and used machine learning techniques for diagnosis and monitoring through voice samples.
Methods:
This Systematic Literature Review (SLR) investigates the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders (VAD) without direct relation to the voice box. Through a comprehensive search string, studies published from 4 Feb. 2012 to 4 Feb. 2022 from the databases Scopus, PubMed, and Web of Science (WoS) were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and two authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis.
Results:
The analysis of the 100 included studies shows that 42% of studies utilized Support Vector Machin, and 64 studies investigated Parkinson’s disease. After 2017, an extended focus on other VADs was observed but still corresponds to a small number of VADs in total.
Conclusions:
Including only peer-reviewed research in English may limit the evidence in this SLR. However, considering under-represented VADs in research and increased focus on monitoring through longitudinal studies with extended and balanced data sets are the gaps in the state-of-the-art literature, which could be beneficial to prioritize in future studies to come one step closer to clinical usage of voice-based diagnostic systems.
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Copyright
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