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Related Experiment Video

Updated: May 5, 2026

Author Spotlight: Implications of Non-Nutritive Sucking on Speech Emergence and Infant Development
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Artificial Intelligence-Based Video Analysis for Assessing Sucking Behavior in Preterm Infants: A Feasibility Study.

Ji Ae Kim1,2, Jihye Chae3, Su Min Kim4

  • 1Department of Physical Medicine and Rehabilitation, Korea University Guro Hospital, Seoul 08308, Republic of Korea.

Children (Basel, Switzerland)
|May 4, 2026
PubMed
Summary
This summary is machine-generated.

An artificial intelligence (AI) tool analyzes bottle-feeding videos to objectively assess sucking behavior in preterm infants. This noninvasive method shows potential for early dysphagia identification in the neonatal intensive care unit (NICU).

Keywords:
artificial intelligenceinfantspretermswallowing

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Area of Science:

  • Neonatal development
  • Medical technology
  • Artificial intelligence in healthcare

Background:

  • Preterm infants frequently exhibit impaired swallowing.
  • Objective assessment tools for neonatal sucking function are limited.
  • Early identification of feeding difficulties is crucial for preterm infant outcomes.

Purpose of the Study:

  • To propose and validate an automated framework using AI to quantitatively assess neonatal sucking behavior.
  • To track facial key points in bottle-feeding videos for objective analysis.
  • To correlate AI-based sucking assessment with developmental outcomes.

Main Methods:

  • Prospective study of 58 preterm infants (corrected age ≤ 2 months).
  • Bottle-feeding videos were analyzed using an AI framework tracking facial key points.
  • Comparison with manual evaluation using the Neonatal Oral Motor Assessment Scale (NOMAS).
  • Developmental outcomes assessed at 12 months corrected age using Bayley Scales of Infant Development (BSID-II).

Main Results:

  • The AI tool achieved high classification accuracy: 82.76% for Normal, 82.76% for Disorganization, and 96.55% for Dysfunction.
  • The AI-based analysis correctly classified 47 out of 58 infants.
  • Lower Psychomotor Development Index (PDI) scores were observed in the Dysfunction group, though not statistically significant.

Conclusions:

  • AI-based video analysis shows preliminary potential as a noninvasive tool for evaluating preterm infant sucking behavior.
  • This automated framework may enable early dysphagia identification by non-specialists in the NICU.
  • Further validation is necessary before widespread clinical implementation of this video-based assessment framework.