ArticleNPJ digital medicine2025
Neonatal pose estimation in the unaltered clinical environment with fusion of RGB, depth and IR images.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Camera-based respiratory rate monitoring in unmodified neonatal intensive care - a cross-sectional study in a UK hospital.EClinicalMedicine · 2026Article
- Automated neonatal sleep positioning assessment by video monitoring and machine learning.Pediatric research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Visual monitoring of pre-term infants in intensive care is critical to ensuring proper development and treatment. Camera systems have been explored for this purpose, with human pose estimation having applications in monitoring position, motion, behaviour and vital signs. Validation in the full range of clinical visual scenarios is necessary to prove real-life utility. We conducted a clinical study to collect RGB, depth and infra-red video from 24 participants with no modifications to clinical care. We propose and train image fusion pose estimation algorithms for locating the torso key-points. Our best-performing approach, a late fusion method, achieves an average precision score of 0.811. Chest covering or side lying decrease the object key-point similarity score by 0.15 and 0.1 respectively, while accounting for 50% and 44% of the time. The baby's positioning and covering supports their development and comfort, and these scenarios should therefore be considered when validating visual monitoring algorithms.
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.