ArticlePediatric research2026
Automated neonatal sleep positioning assessment by video monitoring and machine learning.
Article in Pediatric research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProper positioning during sleep is critical for musculoskeletal and neurological development of preterm neonates, yet current manual assessment methods are time-intensive and subject to inter-observer variability.
methodsIn this proof-of-concept study, we retrospectively investigated body alignment (supine, prone, right lateral, left lateral, semi-prone) and positioning of infants during sleep episodes. We used the Infant Positioning Assessment Tool (IPAT) scoring from recordings obtained during daily routine care at two NICUs. Anatomical keypoints were annotated manually and used for fine-tuning the ViTPose pose estimator and as input for machine learning classifiers to predict alignment and suboptimal positioning.
resultsA total of 1510 images from 50 stable preterm neonates during their quiet periods in closed incubators (gestational age 23-36 weeks) were assessed. Posture classification of five categories achieved a macro F1-score = 0.80 (95% CI: 0.74, 0.86). Suboptimal positioning prediction reached AUROC = 0.86 (0.79, 0.87) and AUPRC = 0.69 (0.59, 0.79). Input image to decision arrangement yielded AUROC = 0.87 (0.84, 0.92) and AUPRC = 0.70 (0.61, 0.82).
conclusionsVideo-based automated posture and positioning assessment is feasible, enabling continuous monitoring and early warning of positioning-related complications. IMPACT: Positioning of preterm neonates is critical for musculoskeletal and neurological development; however, no automated visual assessment model is available. Visual machine learning models can assess infant positioning and predict repositioning needs. Performance saturates across architecturally diverse classifiers, indicating that further gains require improved labeling consistency rather than more complex models. Continuous 24/7 NICU incubator camera images can be used to detect positioning deterioration between routine nursing assessments.
Identifiers
42362701What 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.