AIMC Topic: Child Development

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Social robots as social learning partners: Exploring children's early understanding and learning from social robots.

The Behavioral and brain sciences
Clark and Fischer propose that people interpret social robots not as social agents, but as interactive depictions. Drawing on research focusing on how children selectively learn from social others, we argue that children do not view social robots as ...

Novel AI driven approach to classify infant motor functions.

Scientific reports
The past decade has evinced a boom of computer-based approaches to aid movement assessment in early infancy. Increasing interests have been dedicated to develop AI driven approaches to complement the classic Prechtl general movements assessment (GMA)...

Emergence and organization of adult brain function throughout child development.

NeuroImage
Adult cognitive neuroscience has guided the study of human brain development by identifying regions associated with cognitive functions at maturity. The activity, connectivity, and structure of a region can be compared across ages to characterize the...

Traditional and New Methods of Bone Age Assessment-An Overview.

Journal of clinical research in pediatric endocrinology
Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a...

Predicting brain age with complex networks: From adolescence to adulthood.

NeuroImage
In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brai...

Scientific and Regulatory Considerations for an Ontogeny Knowledge Base for Pediatric Clinical Pharmacology.

Clinical pharmacology and therapeutics
Understanding all aspects of developmental biology, or pediatric ontogeny, that affect drug therapy from the fetus to the adolescent child is the holy grail of pediatric scientists and clinical pharmacologists. The scientific community is now close t...

Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis.

PloS one
BACKGROUND: The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal enviro...

Longitudinal Prediction of Infant Diffusion MRI Data via Graph Convolutional Adversarial Networks.

IEEE transactions on medical imaging
Missing data is a common problem in longitudinal studies due to subject dropouts and failed scans. We present a graph-based convolutional neural network to predict missing diffusion MRI data. In particular, we consider the relationships between sampl...

Regression Convolutional Neural Network for Automated Pediatric Bone Age Assessment From Hand Radiograph.

IEEE journal of biomedical and health informatics
Skeletal bone age assessment is a common clinical practice to investigate endocrinology, and genetic and growth disorders of children. However, clinical interpretation and bone age analyses are time-consuming, labor intensive, and often subject to in...

Robot-based intervention may reduce delay in the production of intransitive gestures in Chinese-speaking preschoolers with autism spectrum disorder.

Molecular autism
BACKGROUND: Past studies have shown that robot-based intervention was effective in improving gestural use in children with autism spectrum disorders (ASD). The present study examined whether children with ASD could catch up to the level of gestural p...