ArticlePerspectives of the ASHA special interest groups2026
Enhancing the assessment of deglutition function in preterm infants with mechano-acoustic analysis and machine learning: a narrative review.
Article in Perspectives of the ASHA special interest groups, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 accurate classification of swallow physiology across the life span. Machine learning algorithms increase diagnostic performance and facilitate 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 diagnostic accuracy and clinical prediction of clinical swallow evaluations (CSEs). Methods: Databases searched included PubMed and Ovid. No filters were placed on year of publication. Results: Twelve relevant records were retrieved for this review. Preliminary studies investigating maturational changes in 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 indicators of deglutition dysfunction in preterm infants and no normative data for healthy, term non-dysphagic 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 diagnostic performance of machine learning algorithms to enhance classification and prediction of deglutition function in preterm infants.
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