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

Abstract

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.

Voice as a Biomarker of Pediatric Health: A Scoping Review

“…India [7] Aggarwal 2020a [13] Aggarwal 2018 [14] Aggarwal 2020 [15] Dubey 2018 [53] Jayasree 2021 [9] Moharir 2017 [62] Sharma 2022 [31] Indonesia [4] Amrulloh 2015 [39] Amrulloh 2015 [43] Amrulloh 2018 [40] Nafisah 2019 [69] Italy [1] Tartarisco 2021 [56] Japan [1] Nakai 2017 [24] Lebanon [1] Salehian Matikolaie 2020 [68] Malaysia [1] Hariharan 2018 [10] Palestine [1] Khalilzad 2022 [66] Poland [4] Kotarba 2020…”

“…Sara et al (2023) [6] discuss the feasibility of remote health monitoring using voice analysis and demonstrate significant advancements in telehealth, leveraging technology, and machine learning for the detection of conditions such as COVID-19 and chronic obstructive pulmonary disease (COPD). Lastly, Idrisoglu et al (2022) [7] present a systematic review on various machine learning models used in voice analysis, finding that Support Vector Machine (SVM) and Neural Network models show high accuracy in diagnosing voice disorders. Despite key findings presented in the current literature, several gaps remain.…”

Voice as an AI Biomarker of Health—Introducing Audiomics

“…Despite significant advances, important limitations currently prevent implementation of voice biomarkers in clinical care. Despite considerable literature 1,2 published especially in the past decade, there is a lack of prospective study and validation of AI algorithms in audiomics on external datasets. These factors, compounded by the limited size, quality, and diversity of existing open-source datasets, likely explain the lack of validated and US Food and Drug Administration-approved AI algorithms for disease detection and monitoring in this space.…”

“…Although the future of voice biomarkers is promising, there remain important limitations to broad integration into clinical care. Within academic research, many studies remain at the level of proof of concept with small- to medium-sized datasets often using voice as the only data type. Comparing studies and pooling data are challenging tasks due to the lack of standards in how we collect voice and speech data.…”