Latest AI and machine learning research in adhd/add for healthcare professionals.
Current ADHD diagnostic practices rely on subjective rating scales and continuous performance tests with limited specificity. We propose an objective deep-learning approach classifying ADHD via task-evoked pupil diameter and binocular eye-movement synchrony during a visual cueing task in 439 participants across 14 clinical centers. We implemented two independent models: a multiple instance learnin...
BACKGROUND: Impulsivity in individuals with attention-deficit/hyperactivity disorder (ADHD) diminishes quality of life. Cortical hypometabolism has been hypothesized to contribute to ADHD. Transcranial photobiomodulation (tPBM) is a safe, non-invasive method for stimulating the prefrontal cortex (PFC), yet its cognition-enhancing effect on impulsivity and sustained attention in adults with ADHD re...
The integration of artificial intelligence (AI) with Traditional Chinese Medicine (TCM) is rapidly expanding, creating new opportunities for digital d...
Psychiatric, neurodevelopmental, and neurodegenerative disorders, including Alzheimer's disease (AD), attention-deficit/hyperactivity disorder (ADHD),...
Pulmonary arterial hypertension (PAH) is a rare, progressive disease of the precapillary pulmonary arteries, characterized by fibroproliferative vascu...
BackgroundArtificial intelligence (AI) continues to emerge into nursing practice with much of the AI research being conducted in the acute care sector...
BACKGROUND: Clinicians spend a substantial share of their working hours on documentation, contributing to workflow inefficiencies, reduced patient-fac...
BACKGROUND: Sustained attention requires continuous engagement and is sensitive to individual differences in motivational processes. Variability in su...
Adaptive functioning typically aligns with cognitive ability in the general population, but this relationship appears more complex in neurodivergent p...
Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental condition requiring early and accurate identification to optimize outco...
One of the key challenges to predict odor from molecular structure is unarguably our limited understanding of the odor space and the complexity of the...
Attention Deficit Hyperactivity Disorder (ADHD), a prevalent neurodevelopmental condition, urgently requires objective biomarkers to improve diagnosti...
OBJECTIVE: The Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group is developing GRADErater (GRADE Rating Automati...
The function and lifetime of moving mechanical assemblies (MMAs) in space depend on the properties of lubricants. MMAs that experience high speeds or ...
BACKGROUND: Aortic enlargement is a powerful predictor of dissection and rupture, yet it is rarely evaluated during routine myocardial perfusion imagi...
This research proposes an empirical benchmarking study of an attention-infused deep convolutional framework for multi-label thoracic pathology classif...
Characterizing associations between individual differences in brain activity and behavior remains a primary challenge in functional neuroimaging resea...
Continued drug use is thought to affect neural networks involved in attention and reward processing, with increased attentional bias being granted to ...
Precision livestock farming (PLF) leverages activity sensors to monitor behaviours like grazing, resting and walking, yet class imbalance in datasets ...
The rapid integration of artificial intelligence into healthcare and education is transforming how nurses teach, learn and acquire knowledge. Despite ...