Latest AI and machine learning research in pediatrics for healthcare professionals.
Digital transformation is fundamentally changing the diagnosis, monitoring and treatment of multiple sclerosis. The integration of multimodal data from imaging, laboratory tests, clinical assessments, patient-reported outcomes and continuous measurements via wearables is creating high-resolution, longitudinal profiles of disease progression. Based on this data, modern analysis methods and artifici...
BACKGROUND AND PURPOSE: The delineation of contrast enhancement in pediatric brain tumors is crucial for effective surgical and treatment planning, as well as for monitoring treatment response per Response Assessment in Pediatric Neuro-Oncology (RAPNO) guidelines. Accurate delineation of enhancement is also important in ground truth generation for training automated deep learning models. However, ...
Callus formation in Lavandula × intermedia varies widely depending on explant type, plant growth regulator composition, and cultivation duration, yet ...
Due to the reliance of deep learning on high-quality datasets and the uncertainty regarding the reproductive period segmentation and image annotation ...
BACKGROUND: This study aims to evaluate the knowledge and attitudes of Mansoura medical students towards artificial intelligence (AI) use in medical e...
BACKGROUND: Artificial intelligence (AI) is increasingly being used in many aspects of society, including health care and education. AI has the potent...
OBJECTIVE: We aimed to develop and evaluate machine learning models to support population-level risk stratification for dental caries in the permanent...
BACKGROUND: Minimally invasive pyeloplasty (MIP), encompassing both conventional laparoscopy and robot-assisted approaches, has become the primary tre...
BACKGROUND: Acne is a chronic skin condition that primarily affects adolescents and young adults but can persist into adulthood. It can have repercuss...
BACKGROUND: Accurate prediction of recurrence in pediatric medulloblastoma remains challenging with clinical variables alone. This study evaluated whe...
UNLABELLED: To systematically evaluate the accuracy, reliability, and clinical applicability of artificial intelligence and large language models (LLM...
PURPOSE OF REVIEW: Children with hereditary polyposis syndromes require long-term endoscopic surveillance to reduce risks of gastrointestinal complica...
Emergency medicine has evolved from an emerging discipline to a formally recognised specialty across much of Asia over the past four decades. Adoption...
BACKGROUND: Social media platforms such as X (formerly Twitter) are increasingly used by journals, authors, and institutions to promote newly publishe...
Chlorophyll content (represented by the Soil and Plant Analyzer Development (SPAD) value) and leaf moisture content (LMC) are two key physiological ph...
Artificial intelligence-based culture reading tools can potentially accelerate reading, improving reporting consistency in image-based interpretation,...
Advances in generative artificial intelligence (AI) have given rise to relational AI-AI agents that mimic human relational capabilities while possessi...
BACKGROUND: Clinical guidelines recommend a stepped-care strategy for patients with hip and knee osteoarthritis that begins with nonoperative approach...
Recent advances in machine learning (ML) have accelerated automated analysis of phonocardiogram (PCG) signals, yet prior surveys often narrow their sc...
OBJECTIVES: Acute otitis media (AOM) is a leading cause of antibiotic prescribing in children although many cases resolve without treatment. 'Watch an...