Enhancing the Assessment of Deglutition Function in Preterm Infants With Mechano-Acoustic Analysis and Machine Learning: A Narrative Review.
Purpose: Clinical indicators of deglutition dysfunction within the context of maturation are not well defined for preterm infants. Mechano-acoustic analysis utilizing cervical auscultation or accelerometry has shown potential for an accurate classification of swallow physiology across the lifespan....
| Publicado en: | Perspectives of the ASHA Special Interest Groups Vol. 11; no. 2; pp. 497 - 511 |
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| Autores principales: | , |
| Formato: | pictorial review tables/charts Journal Article |
| Publicado: |
American Speech-Language-Hearing Association
Apr2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192969906&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192969906 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2381473X KTSD jtl: Perspectives of the ASHA Special Interest Groups issn: 2381473X maglogo: N pubinfo: dt: Apr2026 vid: 11 iid: 2 pid: 42 pub: American Speech-Language-Hearing Association place: Rockville, Maryland artinfo: ui: 192969906 192969906 192969906 10.1044/2025_PERSP-25-00140 192969906 ppf: 497 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Enhancing the Assessment of Deglutition Function in Preterm Infants With Mechano-Acoustic Analysis and Machine Learning: A Narrative Review. aug: au: Bordier, Emily Ortigoza, Eric B. affil: Applied Clinical Research Program, School of Health Professions, UT Southwestern Medical Center, Dallas, TX sug: subj: Deglutition Evaluation Infant, Premature Machine Learning Algorithms Utilization Vibration Acoustics Deglutition Disorders Diagnosis Infant, Newborn Kinematics Infant, Newborn: birth-1 month ab: Purpose: Clinical indicators of deglutition dysfunction within the context of maturation are not well defined for preterm infants. Mechano-acoustic analysis utilizing cervical auscultation or accelerometry has shown potential for an accurate classification of swallow physiology across the lifespan. Machine learning algorithms increase diagnostic performance and facilitate the analysis of more complex deglutition data in adult and pediatric populations. This narrative review will investigate the feasibility and usability of mechano-acoustic analysis combined with machine learning to identify indicators of deglutition impairment in preterm infants to increase the diagnostic accuracy and clinical prediction of clinical swallow evaluations. Method: Databases searched included PubMed and Ovid. No filters were placed on the year of publication. Results: Twelve relevant records were retrieved for this review article. Preliminary studies investigating maturational changes in the mechano-acoustic features of deglutition have yielded significant findings. Research utilizing machine learning to support mechano-acoustic analysis in preterm infants is lacking. There are no published studies investigating the indicators of deglutition dysfunction in preterm infants and no normative data for healthy, term, nondysphagic neonates. Conclusions: Mechano-acoustic analysis is a feasible technique to investigate deglutition performance in preterm infants. Identification of normative values, temporal correlation of signals with deglutition kinematics, and standardization of relevant features in future longitudinal studies will enhance clinical utility. Further study is needed to determine the diagnostic performance of machine learning algorithms to enhance the classification and prediction of deglutition function in preterm infants. pubtype: Academic Journal doctype: pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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