Artificial Intelligence-Based Video Analysis for Assessing Sucking Behavior in Preterm Infants: A Feasibility Study

Preterm infants often experience impaired swallowing function, and objective assessments for this population remain limited. In this prospective single-center study, we aimed to propose and validate an automated framework that quantitatively assesses neonatal sucking behavior by tracking facial key points in bottle feeding videos.

Fifty-eight preterm infants (corrected age [CA] ≤ 2 months) were enrolled, and 2 min videos of bottle-feeding were recorded. Certified therapists manually evaluated the videos using the Neonatal Oral Motor Assessment Scale (NOMAS), and an artificial intelligence (AI)-based analysis classified the videos into the following three groups: Normal, Disorganization, and Dysfunction. At 12 months CA, developmental outcomes were assessed using the Mental Development Index (MDI) and the Psychomotor Development Index (PDI) of the Bayley Scales of Infant Development, Second Edition (BSID-II).

Among the 58 infants, the AI-based tool correctly classified 47 and misclassified 11. The classification accuracy was 82.76 for the Normal group, 82.76 for Disorganization, and 96.55 for Dysfunction. The mean PDI was lower in the Dysfunction group than in other groups; however, the differences were not statistically significant.

This novel AI-based video analysis demonstrates preliminary potential as a noninvasive tool for evaluating sucking behavior in preterm infants, potentially enabling early identification of dysphagia even by non-specialists in the neonatal intensive care unit (NICU) without hazard exposure. This feasibility study demonstrates preliminary technical viability of a video-based framework for neonatal sucking behavior assessment; however, further validation is required before clinical implementation.