Trial reportScientific reports2022
Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.
Trial report in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 31 citations in OpenAlex.
- Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.JAMA network open · 2022Trial
- Nonlinear Measures Applied to Spontaneous Infant Movement Analysis: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Markerless Motion Capture for Human Movement Estimation Using Artificial Intelligence: A Systematic Review.Pediatric reports · 2026Review
- Infants' spontaneous movements explore arm dynamics.Communications biology · 2026Article
- Movement smoothness at term-equivalent age is associated with motor developmental delay in preterm infants.Scientific reports · 2025Article
- Automatic infant 2D pose estimation from videos: Comparing seven deep neural network methods.Behavior research methods · 2025Article
- Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.Scientific reports · 2025Article
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 2025Article
- Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders.Diagnostics (Basel, Switzerland) · 2025Article
- Deep-learning-based markerless tracking of distal anatomical landmarks in clinically recorded videos for assessing infant movement patterns associated with neurodevelopmental status.Journal of the Royal Society of New Zealand · 2025Article
- NeoVault: empowering neonatal research through a neonate data hub.BMC pediatrics · 2024Article
- Artificial intelligence detects awareness of functional relation with the environment in 3 month old babies.Scientific reports · 2024Article
- Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants.Scientific reports · 2024Article
- Spontaneous movements as prognostic tool of neurodevelopmental outcomes in preterm infants: a narrative review.Clinical and experimental pediatrics · 2023Article
- Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study.BMC pediatrics · 2023Article
- Automated Movement Analysis to Predict Cerebral Palsy in Very Preterm Infants: An Ambispective Cohort Study.Children (Basel, Switzerland) · 2022Article
- Neurodevelopmental trajectories from birth to childhood and their impairments: insights from passive, spontaneous, and locomotor-like movements.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The Hammersmith Infant Neurological Examination (HINE) was performed in infants at 4 months of corrected age. Joint positional data were extracted using a pretrained pose-estimation model. Complexity and similarity indices of joint angle and angular velocity in terms of sample entropy and Pearson correlation coefficient were compared between the infants with HINE < 60 and ≥ 60. Video images of spontaneous movements were recorded in 65 preterm infants at term-equivalent age. Complexity indices of joint angles and angular velocities differed between the infants with HINE < 60 and ≥ 60 and correlated positively with HINE scores in most of the joints at the upper and lower extremities (p < 0.05). Similarity indices between each joint angle or joint angular velocity did not differ between the two groups in most of the joints at the upper and lower extremities. Quantitative assessments of spontaneous movements in preterm infants are feasible using a deep learning algorithm and sample entropy. The results indicated that complexity indices of joint movements at both the upper and lower extremities can be potential candidates for detecting developmental outcomes in preterm infants.
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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.