ArticleScientific reports2025
Deep learning-based classification of hemiplegia and diplegia in cerebral palsy using postural control analysis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Telerehabilitation and Face-to-Face Exergame Delivery Modalities to Improve Postural Control in Children with Cerebral Palsy: A Randomised Controlled Trial.Medical sciences (Basel, Switzerland) · 2026Trial
- Social robots in cognitive and speech rehabilitation for children with cerebral palsy: a scoping review.Journal of neuroengineering and rehabilitation · 2025Review
- Machine Learning Methods in Posture-Related Applications in Children up to 12 Years Old: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- PredictMed-CDSS: Artificial Intelligence-Based Decision Support System Predicting the Probability to Develop Neuromuscular Hip Dysplasia.Bioengineering (Basel, Switzerland) · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cerebral palsy (CP) is a neurological condition that affects mobility and motor control, presenting significant challenges for accurate diagnosis, particularly in cases of hemiplegia and diplegia. This study proposes a method of classification utilizing Recurrent Neural Networks (RNNs) to analyze time series force data obtained via an AMTI platform. The proposed research focuses on optimizing these models through advanced techniques such as automatic parameter optimization and data augmentation, improving the accuracy and reliability in classifying these conditions. The results demonstrate the effectiveness of the proposed models in capturing complex temporal dynamics, with the Bidirectional Gated Recurrent Unit (BiGRU) and Long Short-Term Memory (LSTM) model achieving the highest performance, reaching an accuracy of 76.43%. These results outperform traditional approaches and offer a valuable tool for implementation in clinical settings. Moreover, significant differences in postural stability were observed among patients under different visual conditions, underscoring the importance of tailoring therapeutic interventions to each patient's specific needs.
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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 Socratic graph.