Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk
Authors
Keywords
-
Journal
JAMA Network Open
Volume 5, Issue 7, Pages e2221325
Publisher
American Medical Association (AMA)
Online
2022-07-11
DOI
10.1001/jamanetworkopen.2022.21325
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants
- (2022) Hyun Iee Shin et al. Scientific Reports
- Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
- (2022) Yi-Fan Song et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
- In-Motion-App for remote General Movement Assessment: a multi-site observational study
- (2021) Lars Adde et al. BMJ Open
- Usability and inter-rater reliability of the NeuroMotion app: A tool in General Movements Assessments
- (2021) Katarina A. Svensson et al. EUROPEAN JOURNAL OF PAEDIATRIC NEUROLOGY
- Novel AI driven approach to classify infant motor functions
- (2021) Simon Reich et al. Scientific Reports
- Early Intervention for Children Aged 0 to 2 Years With or at High Risk of Cerebral Palsy
- (2021) Catherine Morgan et al. JAMA Pediatrics
- Prediction of outcome from MRI and general movements assessment after hypoxic-ischaemic encephalopathy in low-income and middle-income countries: data from a randomised controlled trial
- (2021) Karoline Aker et al. Archives of Disease in Childhood-Fetal and Neonatal Edition
- Towards human-level performance on automatic pose estimation of infant spontaneous movements
- (2021) Daniel Groos et al. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS
- Inter-observer reliability using the General Movement Assessment is influenced by rater experience
- (2021) C. Peyton et al. EARLY HUMAN DEVELOPMENT
- A Spatio-Temporal Attention-Based Model for Infant Movement Assessment From Videos
- (2021) Binh Nguyen-Thai et al. IEEE Journal of Biomedical and Health Informatics
- Technology-assisted quantification of movement to predict infants at high risk of motor disability: A systematic review
- (2021) Christian B. Redd et al. RESEARCH IN DEVELOPMENTAL DISABILITIES
- Birth Asphyxia Is Associated With Increased Risk of Cerebral Palsy: A Meta-Analysis
- (2020) Shan Zhang et al. Frontiers in Neurology
- Motor outcome after perinatal stroke and early prediction of unilateral spastic cerebral palsy
- (2020) Aurelie Pascal et al. EUROPEAN JOURNAL OF PAEDIATRIC NEUROLOGY
- AI Approaches towards Prechtl’s Assessment of General Movements: A Systematic Literature Review
- (2020) Muhammad Tausif Irshad et al. SENSORS
- Cerebral Palsy: Early Markers of Clinical Phenotype and Functional Outcome
- (2019) Einspieler et al. Journal of Clinical Medicine
- The Predictive Accuracy of the General Movement Assessment for Cerebral Palsy: A Prospective, Observational Study of High-Risk Infants in a Clinical Follow-Up Setting
- (2019) Ragnhild Støen et al. Journal of Clinical Medicine
- Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study
- (2019) Espen A. F. Ihlen et al. Journal of Clinical Medicine
- Unknown
- (2018) DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY
- Parental Perspectives on Diagnosis and Prognosis of Neonatal Intensive Care Unit Graduates with Cerebral Palsy
- (2018) Katherine Guttmann et al. JOURNAL OF PEDIATRICS
- Cerebral Palsy in Extremely Preterm Infants
- (2017) Maria Hafström et al. PEDIATRICS
- Early, Accurate Diagnosis and Early Intervention in Cerebral Palsy
- (2017) Iona Novak et al. JAMA Pediatrics
- Fidgety movements – tiny in appearance, but huge in impact
- (2016) Christa Einspieler et al. Jornal de Pediatria
- The Baby Moves prospective cohort study protocol: using a smartphone application with the General Movements Assessment to predict neurodevelopmental outcomes at age 2 years for extremely preterm or extremely low birthweight infants
- (2016) AJ Spittle et al. BMJ Open
- Unknown
- (2016) DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY
- Unknown
- (2016) DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY
- Are sporadic fidgety movements as clinically relevant as is their absence?
- (2015) Christa Einspieler et al. EARLY HUMAN DEVELOPMENT
- Incidence and Outcomes of Symptomatic Neonatal Arterial Ischemic Stroke
- (2015) S. Grunt et al. PEDIATRICS
- Movement Recognition Technology as a Method of Assessing Spontaneous General Movements in High Risk Infants
- (2015) Claire Marcroft et al. Frontiers in Neurology
- A systematic review of tests to predict cerebral palsy in young children
- (2013) Margot Bosanquet et al. DEVELOPMENTAL MEDICINE AND CHILD NEUROLOGY
- Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings
- (2013) Lars Adde et al. PHYSIOTHERAPY THEORY AND PRACTICE
- Clinical Prognostic Messages From a Systematic Review on Cerebral Palsy
- (2012) I. Novak et al. PEDIATRICS
- Do children really recover better? Neurobehavioural plasticity after early brain insult
- (2011) V. Anderson et al. BRAIN
- The Quality of the Early Motor Repertoire in Preterm Infants Predicts Minor Neurologic Dysfunction at School Age
- (2008) Janneke L.M. Bruggink et al. JOURNAL OF PEDIATRICS
Find Funding. Review Successful Grants.
Explore over 25,000 new funding opportunities and over 6,000,000 successful grants.
ExploreBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started