Version 1
: Received: 7 May 2024 / Approved: 13 May 2024 / Online: 14 May 2024 (05:04:59 CEST)
How to cite:
Rogers, H. P.; Hseu, A.; Kim, J.; Silberholz, E.; Jo, S.; Dorste, A.; Jenkins, K. Voice as a Biomarker of Pediatric Health: A Scoping Review. Preprints2024, 2024050889. https://doi.org/10.20944/preprints202405.0889.v1
Rogers, H. P.; Hseu, A.; Kim, J.; Silberholz, E.; Jo, S.; Dorste, A.; Jenkins, K. Voice as a Biomarker of Pediatric Health: A Scoping Review. Preprints 2024, 2024050889. https://doi.org/10.20944/preprints202405.0889.v1
Rogers, H. P.; Hseu, A.; Kim, J.; Silberholz, E.; Jo, S.; Dorste, A.; Jenkins, K. Voice as a Biomarker of Pediatric Health: A Scoping Review. Preprints2024, 2024050889. https://doi.org/10.20944/preprints202405.0889.v1
APA Style
Rogers, H. P., Hseu, A., Kim, J., Silberholz, E., Jo, S., Dorste, A., & Jenkins, K. (2024). Voice as a Biomarker of Pediatric Health: A Scoping Review. Preprints. https://doi.org/10.20944/preprints202405.0889.v1
Chicago/Turabian Style
Rogers, H. P., Anna Dorste and Kathy Jenkins. 2024 "Voice as a Biomarker of Pediatric Health: A Scoping Review" Preprints. https://doi.org/10.20944/preprints202405.0889.v1
Abstract
The human voice has the potential to serve as a valuable biomarker for the early detection, diagnosis, and monitoring of pediatric conditions. This scoping review synthesizes the current knowledge on the application of Artificial Intelligence (AI) in analyzing pediatric voice as a biomarker for health. The included studies featured voice recordings from pediatric populations aged 0-17 years, utilized feature extraction methods, and analyzed pathological biomarkers using AI models. Data from 62 studies were extracted, encompassing study and participant characteristics, recording sources, feature extraction methods, and AI models. The review showed a global representation of pediatric voice studies, with a focus on developmental, respiratory, speech, and language conditions. The most frequently studied conditions were Autism Spectrum Disorder, intellectual disabilities, asphyxia, and asthma. Mel-Frequency Cepstral Coefficients were the most utilized feature extraction method, while Support Vector Machines were the predominant AI model. The analysis of pediatric voice using AI demonstrates promise as a non-invasive, cost-effective biomarker for a broad spectrum of pediatric conditions. However, further research and development are crucial to enhance the accuracy and applicability of these tools in clinical settings.
Medicine and Pharmacology, Pediatrics, Perinatology and Child Health
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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