AIMC Topic: Neurodevelopmental Disorders
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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
Apr 29, 2025
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...
Using machine learning to investigate the influence of the prenatal chemical exposome on neurodevelopment of young children.
Neurotoxicology
Apr 11, 2025
Research investigating the prenatal chemical exposome and child neurodevelopment has typically focused on a limited number of chemical exposures and controlled for sociodemographic factors and maternal mental health. Emerging machine learning approac...
Prenatal Diagnostics Using Deep Learning: A Dual Approach to Plane Localization and Cerebellum Segmentation in Ultrasound Images.
Journal of clinical ultrasound : JCU
Feb 3, 2025
OBJECTIVE: The fetal ultrasound examination is the significant task of mid-term pregnancy inspection and the accurate localization as well as the segmentation of the cerebellum is crucial for clinical diagnosis. This research focuses on developing de...
Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field.
Journal of neurodevelopmental disorders
Nov 15, 2024
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
Nov 7, 2024
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 ...
Joint self-supervised and supervised contrastive learning for multimodal MRI data: Towards predicting abnormal neurodevelopment.
Artificial intelligence in medicine
Sep 30, 2024
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 ...
Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.
Scientific reports
Jun 18, 2024
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...
Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.
European radiology
Nov 14, 2023
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...
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
Apr 17, 2023
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...
Deep learning is widely applicable to phenotyping embryonic development and disease.
Development (Cambridge, England)
Nov 5, 2021
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...
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