ADHD/ADD

A Multimodal Dataset for Neurophysiological and AI Applications.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity. Current diagnostic methods rely primarily on subjective clinical evaluations, which are prone to bias. Neurophysiological techniques such as electroencephalography (EEG), eye tracking, and electrodermal activity (EDA) offer promising objective alt...

Smartphone-Based Portable Slit Lamp in Anterior Segment Diseases: A Narrative Review of Clinical Assessment and Integration with Artificial Intelligence.

Smartphone-based portable slit lamps are rapidly evolving from simple add-ons into practical, low-cost front-line tools for anterior segment care beyond traditional clinical settings. By integrating high-performance smartphone cameras with compact optical attachments (e.g., slit-light converters, macro lenses, blue filters) and dedicated applications, these devices deliver slit-lamp-style imaging ...

Automatic classification method of e-commerce commodity raw materials through the introduction of self-supervised concepts and the construction of domain ontology.

The e-commerce platform's function-oriented classification basis will cause items with the same (different) raw materials to be incorrectly classified...

Cardiovascular Risk Assessment Tools in Chronic Kidney Disease.

Cardiovascular (CV) risk calculators estimate the likelihood of CV events by integrating factors such as age, sex, blood pressure, lipids, smoking, an...

Dual-kingdom necrobiome succession extends postmortem interval estimation into skeletonization.

Forensic estimation of the postmortem interval (PMI) becomes increasingly challenging when decomposition progresses beyond the initial weeks, as tradi...

Predicting the usage of seasonal drugs by utilizing advanced machine learning: An albuterol case study.

Manual forecasting of seasonal medication demand results in inefficiencies and labor burden. With the advancement of machine learning, there is an opp...

Tailored psychotherapy and AI-enhanced contingency management for co-occurring disorders in cannabis use disorder: a systematic review.

BACKGROUND: Cannabis use disorder (CUD) commonly co-occurs with depression, post-traumatic stress disorder (PTSD), anxiety, and attention-deficit/hype...

Incremental prognostic value of immune cell densities beyond clinical parameters in non-small cell lung cancer.

BACKGROUND: Multiplex immunofluorescence imaging enables detailed characterization of the tumor immune microenvironment, but whether immune cell densi...

Fracture risk scores using output from an opportunistic screen of low bone density from conventional X-ray.

Fracture risk is commonly assessed by FRAX, a tool that estimates 10-yr risk for major osteoporotic fracture (MOF) and hip fracture. FRAX scores are o...

Refined trajectory smoothing and deep learning classification of human sperm motility.

STUDY QUESTION: Can precise trajectory smoothing improve extraction of sperm motility features, and can deep learning on raw trajectory data enable ac...

The role and utility of artificial intelligence and machine learning for diagnostic prediction in general practice.

Diagnostic prediction models are commonly used in general practice to support clinical decision-making. Traditionally, these models have been develope...

Deep residual network enhanced with multilevel residual-of-residual for automatic classification of radio signals for 5G and beyond systems.

Automatic Modulation Classification (AMC) plays a critical role in the design of intelligent receivers for next-generation wireless systems, particula...

Classification and discrimination of emotion dysregulation disorders using machine learning.

Attention-deficit/hyperactivity (ADHD), bipolar (BD) and borderline personality (BPD) disorders are severe psychiatric illnesses often presenting with...

Machine learning prediction of food addiction in university students using demographic, anthropometric and personality traits.

People's eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose...

Development and multi-center validation of a school-home integrated machine learning model for early screening of attention-deficit/hyperactivity disorder in school-aged children.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is frequently under-identified in community settings, delaying necessary intervention. Thi...

Human-AI interaction and collaboration in radiology: from conceptual frameworks to responsible implementation.

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction w...

Trust at risk: Why public health must lead the use of AI in pandemic preparedness.

Artificial intelligence (AI) is poised to play a transformative role in pandemic preparedness, with the potential to enhance surveillance, risk assess...

Syntactic complexity of written text as a structural linguistic marker of depressive symptoms.

BACKGROUND: Language analysis methods have been increasingly explored for the detection of depressive symptoms. However, current approaches have large...

White matter microstructure predicts effort and reward sensitivity.

From rodents to humans, animals constantly face a central question: is the reward worth the effort? Effort and reward sensitivity in such situations v...

On machine learning based QSPR analysis of amphetamine derivatives using regression models.

In this study, we show the quantitative structure-property relationship (QSPR) for amphetamine derivatives based on neighborhood degree-based topologi...