Latest AI and machine learning research in pediatrics for healthcare professionals.
Attention Deficit Hyperactivity Disorder (ADHD), a prevalent neurodevelopmental condition, urgently requires objective biomarkers to improve diagnostic precision and treatment monitoring. This systematic review evaluates the utility of task-state functional near-infrared spectroscopy (fNIRS) in identifying neurobiological biomarkers for ADHD in children and adolescents. Following PRISMA 2020 guide...
This study examined inhibitory control development across two samples of Chinese children: a cross-sectional (n = 1,122; 45.5% female; 91.9% Han; Mage=12.42 years, range: 6.0-18.7 years) with 6-month longitudinal follow-up and independent validation sample (n = 1,026; 45.1% female; 90.8% Han; Mage=12.44 years, range: 6.1-18.8 years). Generalized additive models applied to Stroop and Go/No-Go tasks...
BACKGROUND: Asynchronous online forums provide flexible, accessible peer support for many people living with dementia and carers. Moderators are centr...
BACKGROUND: The increasing documentation burden on physicians is a significant contributor to burnout and decreases in care quality. Artificial intell...
BACKGROUND: The global COVID-19 vaccine rollout faces challenges from persistent hesitancy, especially in rural and underserved regions. Alaska's uniq...
Silicon growth on Ag(111) is a prototypical system for exploring silicene-like monolayers and complex Si-metal interfaces, yet its atomistic nucleatio...
The recent rapid development of mobile and wearable sensing technologies and computational modeling has allowed for high-density and continuous measur...
RATIONALE AND OBJECTIVES: Manual Cobb angle measurement can exhibit high interrater variability, potentially affecting scoliosis management decisions....
Pediatric fracture detection in plain radiographs presents distinct clinical challenges due to the presence of growth plates, incomplete ossification,...
Traditional talent identification in sprinting relies heavily on chronological performance metrics, which are often confounded by biological maturatio...
We developed and validated PANDA, a deep-learning model for pediatric arousal detection in polysomnography. PANDA uses a U-Net-style encoder-decoder a...
Fetal congenital heart disease (FCHD) remains a leading cause of infant mortality globally, yet the clinical deployment of deep learning models for au...
BACKGROUND: To achieve a transition toward sustainable development, education is an essential driver of change. Factors like learners' attitudes, cult...
OBJECTIVE: To develop and evaluate a multimodal deep learning model that integrates first- and second-trimester ultrasound images with first-trimester...
BACKGROUND/AIM: To determine the comparative efficacy of trained versus untrained generative artificial intelligence platforms in providing multiple-c...
OBJECTIVE: To synthesize contemporary developments in head and neck oncologic free flap reconstruction, with emphasis on perioperative physiologic opt...
BACKGROUND: Artificial intelligence (AI) is increasingly incorporated into medical education. Widely available, general-purpose generative AI applicat...
PURPOSE: MRI detection of subtle focal cortical dysplasia (FCD)-like abnormalities remains challenging in focal epilepsy. Higher signal-to-noise ratio...
Goldenhar Syndrome (GS) is a rare congenital craniofacial disorder characterized by asymmetrical facial deformities. It is often underdiagnosed due to...
Sudden Cardiac Death (SCD) remains a leading cause of mortality worldwide, with outcomes critically dependent on the effective implementation of the "...