ADHD/ADD

Machine learning-based screening model for Tic disorders comorbid with attention-deficit/hyperactivity disorder in children and SHAP value interpretation: a retrospective observational study.

Tic disorders (TD) are common neurodevelopmental conditions in children and adolescents, characterized primarily by motor and vocal tics, with heterogeneous symptom presentations and significant fluctuations. Concurrently, attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent comorbidity of TD, often exacerbating academic and emotional difficulties. However, due to the potential ma...

Deep chemical structure graph learning deciphers the lipotoxicity code of hypertriglyceridemic pancreatitis.

The discrepancy between serum triglyceride levels and the clinical severity of hyperlipidemic acute pancreatitis (HLAP) complicates risk stratification. Traditional lipidomics, which primarily rely on linear abundance, often fail to distinguish the HLAP-specific lipidome from the metabolic background of hypertriglyceridemia (HTG). To overcome this limitation, DeepLipiDecipher was developed as a kn...

Artificial Intelligence in Neuroradiology: A Review of FDA-regulated Algorithms.

Artificial intelligence (AI) is increasingly integrated into neuroradiology practice, with a growing number of FDA-cleared algorithms now supporting t...

Classification of familial and non-familial ADHD using auto-encoding network and binary hypothesis testing.

Family heritage is one of the most powerful risk factors for attention-deficit/hyperactivity disorder (ADHD). Children with familial ADHD (ADHD-F) and...

Vision-language models as an integrative layer for clinical artificial intelligence in radiology: A systems-level perspective.

Clinical adoption of artificial intelligence (AI) in radiology has matured through task-specific tools for detection, segmentation, triage, and quanti...

Gated edge-node interaction graph convolutional network for ADHD classification.

Objective.Attention deficit hyperactivity disorder (ADHD) remains challenging to diagnose objectively and often relies on subjective clinical assessme...

Stabilizing neuromorphic ECG processors via adaptive fractional fatigue.

Spiking Neural Networks (SNNs) deployed on wearable devices can exhibit runaway firing when processing noisy electrocardiogram (ECG) signals, increasi...

Altered EEG markers of reward learning during abstinence in alcohol dependence: A probabilistic reversal learning study.

OBJECTIVE: This study investigated neurophysiological and behavioural adaptations in reward learning and decision making which may contribute to the d...

Large Language Models in Mental Health Research and Treatment.

Mental health challenges add immensely to the global burden of disease, yet traditional approaches to psychological assessment and care remain resourc...

Cross-species insights from ART-D to uncover evolutionarily conserved oncogenic mechanisms.

Cancer arises from oncogenic clones, yet the dynamic mechanisms driving their stepwise evolution toward malignancy remain incompletely understood. Her...

Single-cell and machine learning reveal ROS-associated heterogeneity in hepatocellular carcinoma for precision medicine.

BACKGROUND: Reactive oxygen species (ROS) are critical regulators of tumor development and progression, yet their clinical significance and cellular m...

Neural Motion Path: a Path-based Authoring System for Pre-rendered Character Animation.

Authoring high-quality character animation is essential in multimedia production, especially for pre-rendered formats such as films and TV series. The...

Coupling generative and predictive machine learning algorithms to enhance haloacetonitriles prediction in small water systems.

To overcome data scarcity constraints in modeling emerging disinfection byproducts (EDBPs) for resource-limited small water systems, this study introd...

Translating innovation into payment: A.I. reimbursement in U.S. health care.

Artificial intelligence (A.I.) technologies are increasingly deployed across clinical care, yet reimbursement remains a major barrier to sustainable a...

What do clinicians edit in ambient AI-drafted clinical documentation? A qualitative content analysis.

OBJECTIVE: Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but ho...

Classifying ADHD Presentations Using Temporally Segmented Behavioral Data from a Serious Game.

Distinguishing attention deficit/hyperactivity disorder (ADHD) presentations, such as predominantly inattentive (ADHD-I) and hyperactive/impulsive (AD...

ADHD identification from real-life recorded video.

Attention Deficit Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that affects more than 10% of the population. Accurate and early diag...

[Artificial intelligence in medicine : Foundations, potential, structural limits and current developments].

Artificial intelligence (AI) is widely regarded as a key technology for the further development of medical care. Its performance, however, depends les...

The expected benefits of using social robots when working with institutionalised older adults: A qualitative study among healthcare professionals.

The shortage of healthcare professionals increasingly challenges the provision of care for institutionalised older adults. This study investigates hea...

RAN: A randomness-anchored watermark attacking network with stealth and effectiveness.

Research on watermark attacking is essential to reinforce robust watermarking methods by providing new attacking benchmarks. Recently, there is an eme...