{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T13:25:30Z","timestamp":1792070730146,"version":"4.4.0"},"reference-count":70,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T00:00:00Z","timestamp":1717459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health","award":["1OT20D032720-01"],"award-info":[{"award-number":["1OT20D032720-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Children"],"abstract":"<jats:p>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\u201317 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. Data from 39 models across 35 studies were evaluated for accuracy, sensitivity, and specificity. 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. Further research is necessary to standardize the feature extraction methods and AI models utilized for the evaluation of pediatric voice as a biomarker for health. Standardization has significant potential to enhance the accuracy and applicability of these tools in clinical settings across a variety of conditions and voice recording types. Further development of this field has enormous potential for the creation of innovative diagnostic tools and interventions for pediatric populations globally.<\/jats:p>","DOI":"10.3390\/children11060684","type":"journal-article","created":{"date-parts":[[2024,6,4]],"date-time":"2024-06-04T11:48:50Z","timestamp":1717501730000},"page":"684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Voice as a Biomarker of Pediatric Health: A Scoping Review"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7853-633X","authenticated-orcid":false,"given":"Hannah Paige","family":"Rogers","sequence":"first","affiliation":[{"name":"Department of Cardiology, Boston Children\u2019s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne","family":"Hseu","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Boston Children\u2019s Hospital, 333 Longwood Ave, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth","family":"Silberholz","sequence":"additional","affiliation":[{"name":"Department of Pediatrics, Boston Children\u2019s Hospital, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6873-7541","authenticated-orcid":false,"given":"Stacy","family":"Jo","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology, Boston Children\u2019s Hospital, 333 Longwood Ave, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Dorste","sequence":"additional","affiliation":[{"name":"Boston Children\u2019s Hospital, 300 Longwood Avenue, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8702-988X","authenticated-orcid":false,"given":"Kathy","family":"Jenkins","sequence":"additional","affiliation":[{"name":"Department of Cardiology, Boston Children\u2019s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19066","DOI":"10.1038\/s41598-019-55271-y","article-title":"Phonetic relevance and phonemic grouping of speech in the automatic detection of Parkinson\u2019s Disease","volume":"9","author":"Dehak","year":"2019","journal-title":"Sci. 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