ReviewFrontiers in neurology2017
Multivariate Analysis and Machine Learning in Cerebral Palsy Research.
Review in Frontiers in neurology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Pooled it
- Attention over vulnerable brain regions associating cerebral palsy disorder and biological markers.Journal of advanced research · 2025Article
- Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.Sensors (Basel, Switzerland) · 2024Review
- Trends in brain MRI and CP association using deep learning.La Radiologia medica · 2024Article
- Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.BMJ paediatrics open · 2024Article
- Emerging methods for measuring physical activity using accelerometry in children and adolescents with neuromotor disorders: a narrative review.Journal of neuroengineering and rehabilitation · 2024Review
- Artificial intelligence and machine learning approaches in cerebral palsy diagnosis, prognosis, and management: a comprehensive review.PeerJ. Computer science · 2024Review
- Identifying treatment non-responders based on pre-treatment gait characteristics - A machine learning approach.Heliyon · 2023Article
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Estimation of Gross Motor Functions in Children with Cerebral Palsy Using Zebris FDM-T Treadmill.Journal of clinical medicine · 2022Article
- A Review on Recent Advances of Cerebral Palsy.Oxidative medicine and cellular longevity · 2022Review
- AI Approaches Towards Prechtl's Assessment of General Movements: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebral palsy (CP), a common pediatric movement disorder, causes the most severe physical disability in children. Early diagnosis in high-risk infants is critical for early intervention and possible early recovery. In recent years, multivariate analytic and machine learning (ML) approaches have been increasingly used in CP research. This paper aims to identify such multivariate studies and provide an overview of this relatively young field. Studies reviewed in this paper have demonstrated that multivariate analytic methods are useful in identification of risk factors, detection of CP, movement assessment for CP prediction, and outcome assessment, and ML approaches have made it possible to automatically identify movement impairments in high-risk infants. In addition, outcome predictors for surgical treatments have been identified by multivariate outcome studies. To make the multivariate and ML approaches useful in clinical settings, further research with large samples is needed to verify and improve these multivariate methods in risk factor identification, CP detection, movement assessment, and outcome evaluation or prediction. As multivariate analysis, ML and data processing technologies advance in the era of Big Data of this century, it is expected that multivariate analysis and ML will play a bigger role in improving the diagnosis and treatment of CP to reduce mortality and morbidity rates, and enhance patient care for children with CP.
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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.