AIMC Topic: Neurodevelopmental Disorders

Clear Filters Showing 11 to 20 of 20 articles

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...

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...

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...

A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.

Scientific reports
Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodev...

Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.

NeuroImage
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...

Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data.

NeuroImage
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brai...

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

NeuroImage
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is compo...

protPheMut: An Interpretable Machine Learning Tool for Classification of Cancer and Neurodevelopmental Disorders in Human Missense Mutations.

Journal of chemical information and modeling
Recent advances in human genomics have revealed that missense mutations in a single protein can lead to distinctly different phenotypes. In particular, some mutations in oncoproteins like MEK1, MEK2, PI3Kα, PTEN, SHAP2, and RAS are linked various can...

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...

Identification of haploinsufficient genes from epigenomic data using deep forest.

Briefings in bioinformatics
Haploinsufficiency, wherein a single allele is not enough to maintain normal functions, can lead to many diseases including cancers and neurodevelopmental disorders. Recently, computational methods for identifying haploinsufficiency have been develop...