ADHD/ADD

ADBrainNet: a deep neural network for Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) classification using resting-state fMRI images based on explainable artificial intelligence.

Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) are two psychiatric disorders frequently encountered in children. ADHD is further categorized into three subtypes. The diagnostic processes for these conditions are complex and often prone to misclassification. We proposed a lightweight deep neural network, ADBrainNet, to differentiate ASD, ADHD combined, ADHD h...

Leveraging generative AI to transform statistical analysis plan authoring in clinical trials.

BackgroundStatistical analysis plans are critical regulatory documents that define the statistical methodology, objectives, and data-handling procedures for clinical trials. The traditional process of statistical analysis plan development is resource-intensive, typically spanning 4-6 weeks, and is increasingly complicated by evolving regulatory requirements and complex trial designs such as adapti...

AI-Powered Ambient Scribe Technology Experiences Among Emergency Physicians: Cross-Sectional, Mixed Methods Pilot Survey Study.

BACKGROUND: Health care organizations have started to implement artificial intelligence-powered ambient scribe technology in clinical documentation wo...

Computational frameworks for automated detection and quantification of paroxysmal sympathetic hyperactivity among traumatic brain injury patients.

Paroxysmal sympathetic hyperactivity (PSH) is a syndrome that occurs in a large subset of critically ill traumatic brain injury (TBI) patients and is ...

[Cerebral neuromodulation in neurodevelopmental disorders].

Neurodevelopmental disorders comprise a heterogeneous group of conditions characterized by earlyonset impairments in cognitive, behavioral, emotional,...

Single-nucleus multi-omics dissection of dysregulated trophoblast development and disrupted immune microenvironment in complete hydatidiform moles.

Hydatidiform mole (HM) is driven by aberrant trophoblast proliferation, disrupting embryonic development and leading to pregnancy loss with increased ...

Artificial intelligence for personalized multiple micronutrient supplementation in maternal health.

Maternal undernutrition and micronutrient deficiencies remain pervasive, contributing to adverse pregnancy outcomes and long-term health risks for mot...

Voices of change: associations between vocal markers and symptoms of ADHD - Findings from the LIFE child study.

In the context of precision psychiatry, voice characteristics in psychiatric disorders have significant potential as diagnostic markers. However, most...

Using machine learning to identify the strongest personal, school, and family correlates of youth mental health and well-being.

Research has identified numerous personal, school, and family factors associated with youth mental health, yet it remains unclear which correlates bes...

Attention-Enhanced Temporal and Spatial Feature Extraction Network for ADHD Diagnosis based on fMRI.

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder, and accurate diagnosis is critical for ensuring timely int...

Combining multimodal fatigue fracture surface images for analysis with a CNN.

This work uses three different modalities, namely SEM, BSE and scanning white light interference (SWLI) to image fatigue fracture surfaces of Ti-6Al-4...

Encoding and Decoding of Brain Dynamic Functional Connectivity for ADHD Diagnosis.

Recent studies have demonstrated strong associations between the changes in dynamic functional connectivity (FC) and both behavioral and cognitive fun...

System-Specific Reparameterization of Density Functionals with Machine Learning: Application to Spin-Splitting Energies of Transition Metal Complexes.

Accurate estimates of the spin-splitting energy (SSE) are essential for proper modeling of transition metal complex (TMC) catalysts and functional mat...

Early detection of adults ADHD using electronic health records: A machine learning study.

Digital pathology-based artificial intelligence algorithms in prostate cancer: inside the 'black box'.

Artificial intelligence (AI) algorithms leveraging digital pathology slides are currently transforming the way urological cancers are diagnosed and gr...

MRI-based Radiomics and volumetrics for predicting the onset of Alzheimer's Disease with explainable machine learning.

The detection of Alzheimer's Disease (AD) using structural Magnetic Resonance Imaging (MRI) and Machine Learning (ML) often focuses on late-stage atro...

Chronic oil pollution in small ports: A review of the role and potential of AUVs.

Chronic oil pollution in small ports are typically low-level, repeated, and long-lasting, making them difficult to monitor with conventional approache...

Deep learning-based object detection of dental implant systems in panoramic and periapical radiographs.

STATEMENT OF PROBLEM: The manual identification of dental implant systems on radiographs is time-consuming, operator-dependent, and prone to diagnosti...

Plasma ATN biomarkers across the alzheimer's disease continuum in a Chilean community- and clinic-based cohort.

BACKGROUND: Plasma biomarkers have emerged as robust indicators of Alzheimer's disease (AD) pathology, offering accessible tools for staging and strat...

Electrochemical biosensors for Calreticulin in lung Cancer.

Electrochemical biosensors represent an advanced and novel technology for the sensitive and specific detection of clinically relevant biomarkers. Of t...