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Neurodevelopmental Disorders

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A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.

International journal of environmental research and public health
In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders' involvement in the environment during everyday living activities. In order to support the children an...

Deep learning is widely applicable to phenotyping embryonic development and disease.

Development (Cambridge, England)
Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of altered embryonic development remains a challenge. Here, we explore how deep learning (U-Net) can auto...

The Role of Robotic Rehabilitation in Children with Neurodevelopmental Disorders.

Psychiatria Danubina
In the last years, traditional treatments have been combined with innovative therapies, such as robot-assisted training, an interesting new rehabilitation tool for children with neurologic impairment. The robots deliver a high dose of training and in...

Leveraging Knowledge Graphs and Natural Language Processing for Automated Web Resource Labeling and Knowledge Mobilization in Neurodevelopmental Disorders: Development and Usability Study.

Journal of medical Internet research
BACKGROUND: Patients and families need to be provided with trusted information more than ever with the abundance of online information. Several organizations aim to build databases that can be searched based on the needs of target groups. One such gr...

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

European radiology
OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphom...

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Scientific reports
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...

Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.

Artificial intelligence in medicine
The integration of different imaging modalities, such as structural, diffusion tensor, and functional magnetic resonance imaging, with deep learning models has yielded promising outcomes in discerning phenotypic characteristics and enhancing disease ...

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.

Journal of neurodevelopmental disorders
Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ...

Machine-learning based prediction of future outcome using multimodal MRI during early childhood.

Seminars in fetal & neonatal medicine
The human brain undergoes rapid changes from the fetal stage to two years postnatally, during which proper structural and functional maturation lays the foundation for later cognitive and behavioral development. Multimodal magnetic resonance imaging ...

Topology-Guided Graph Masked Autoencoder Learning for Population-Based Neurodevelopmental Disorder Diagnosis.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Exploring the pathogenic mechanisms of brain disorders within population is an important research in the field of neuroscience. Existing methods either combine clinical information to assist analysis or use data augmentation for sample expansion, ign...