ArticleChildren (Basel, Switzerland)2026
Artificial Intelligence-Based Video Analysis for Assessing Sucking Behavior in Preterm Infants: A Feasibility Study.
Article in Children (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence (AI) Implementation in Maternal and Child Health: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
BACKGROUND/
objectivesPreterm 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.
methodsFifty-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).
resultsAmong 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.
conclusionsThis 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.