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The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

PloS one
BACKGROUND: Children with high autistic traits often exhibit deficits in drawing, an important skill for social adaptability. Machine learning is a powerful technique for learning predictive models from movement data, so drawing processes and product...

AI-Driven Care Navigation to Foster Early Childhood Resilience and Positive Childhood Experiences.

Studies in health technology and informatics
Early life experiences are crucial for health and well-being, influencing physical, emotional, and social development throughout the lifespan. Recent research shows that Positive Childhood Experiences (PCEs), such as access to supportive environments...

Leveraging artificial intelligence for diagnosis of children autism through facial expressions.

Scientific reports
The global population contains a substantial number of individuals who experience autism spectrum disorder, thus requiring immediate identification to enable successful intervention approaches. The authors assess the detection of autism-related learn...

Comparison of deep learning models for facial attractiveness assessment on 3D photos.

Journal of dentistry
OBJECTIVES: Convolutional neural networks (CNNs) have demonstrated remarkable success in orthodontics. This study aimed to evaluate the accuracy and precision of several prominent CNN models for evaluating the facial attractiveness in Chinese orthodo...

Explainable transformer-based deep survival analysis in childhood acute lymphoblastic leukemia.

Computers in biology and medicine
BACKGROUND: Acute lymphoblastic leukemia (ALL) is the most common type of leukemia among children and adolescents and can be life-threatening. The incidence of new cases has been increasing in recent years. Developing a predictive model to forecast t...

Unique and shared transcriptomic signatures underlying localized scleroderma pathogenesis identified using interpretable machine learning.

JCI insight
Using transcriptomic profiling at single-cell resolution, we investigated cell-intrinsic and cell-extrinsic signatures associated with pathogenesis and inflammation-driven fibrosis in both adult and pediatric patients with localized scleroderma (LS)....

Prediction of moderate to severe bleeding risk in pediatric immune thrombocytopenia using machine learning.

European journal of pediatrics
UNLABELLED: This study aimed to develop and validate a risk prediction model for moderate to severe bleeding in children with immune thrombocytopenia (ITP). Data from 286 ITP patients were prospectively collected and randomly split into training (80%...

Supporting Speech-Language Pathologists in Schools With Interdisciplinary Team Science: A Viewpoint From the National Artificial Intelligence Institute for Exceptional Education.

Language, speech, and hearing services in schools
PURPOSE: Complex scientific problems, including those facing the discipline of communication sciences and disorders (CSD), require interdisciplinary teams of scientists who bring diverse perspectives, knowledge, and skills. According to a recent surv...

Prediction of first attempt of suicide in early adolescence using machine learning.

Journal of affective disorders
BACKGROUND: Suicide is the second leading cause of death among early adolescents, yet the first onset of suicide attempts during this critical developmental period remains poorly understood. This study aimed to identify key characteristics associated...

Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles.

Scientific reports
Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying biological complexity. This study explored machine learning approaches to enhance ASD screening acc...