Automated detection of ADHD: Current trends and future perspective
Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit …
[HTML][HTML] Artificial intelligence enabled personalised assistive tools to enhance education of children with neurodevelopmental disorders—a review
Mental disorders (MDs) with onset in childhood or adolescence include
neurodevelopmental disorders (NDDs)(intellectual disability and specific learning …
neurodevelopmental disorders (NDDs)(intellectual disability and specific learning …
[HTML][HTML] Application of deep learning models for automated identification of Parkinson's disease: A review (2011–2021)
Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting
over 6 million people globally. Although there are symptomatic treatments that can increase …
over 6 million people globally. Although there are symptomatic treatments that can increase …
PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework
AJM Dingemans, M Hinne, KMG Truijen, L Goltstein… - Nature Genetics, 2023 - nature.com
Several molecular and phenotypic algorithms exist that establish genotype–phenotype
correlations, including facial recognition tools. However, no unified framework that …
correlations, including facial recognition tools. However, no unified framework that …
[HTML][HTML] Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders
Most genes associated with neurodevelopmental disorders (NDDs) were identified with an
excess of de novo mutations (DNMs) but the significance in case–control mutation burden …
excess of de novo mutations (DNMs) but the significance in case–control mutation burden …
Automated ASD detection using hybrid deep lightweight features extracted from EEG signals
Background Autism spectrum disorder is a common group of conditions affecting about one
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
in 54 children. Electroencephalogram (EEG) signals from children with autism have a …
Epilepsy detection in 121 patient populations using hypercube pattern from EEG signals
Background Epilepsy is one of the most commonly seen neurologic disorders worldwide
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
and has generally caused seizures. Electroencephalography (EEG) is widely used in …
Association of shared decision-making on patient-reported health outcomes and healthcare utilization
Background Shared decision-making (SDM) is a process that respects the rights of patients
to be fully involved in decisions about their care. By evaluating all available healthcare …
to be fully involved in decisions about their care. By evaluating all available healthcare …
Delivery of dark material to Vesta via carbonaceous chondritic impacts
NASA's Dawn spacecraft observations of Asteroid (4) Vesta reveal a surface with the highest
albedo and color variation of any asteroid we have observed so far. Terrains rich in low …
albedo and color variation of any asteroid we have observed so far. Terrains rich in low …
Automated accurate detection of depression using twin Pascal's triangles lattice pattern with EEG Signals
Electroencephalogram (EEG)-based major depressive disorder (MDD) machine learning
detection models can objectively differentiate MDD from healthy controls but are limited by …
detection models can objectively differentiate MDD from healthy controls but are limited by …