Latest AI and machine learning research in pediatrics for healthcare professionals.
Machine learning, a subfield of artificial intelligence, is a rapidly evolving technology that offers great potential for expanding the quality and value of pediatric radiology. We describe specific types of learning, including supervised, unsupervised and semisupervised. Subsequently, we illustrate two core concepts for the reader: data partitioning and under/overfitting. We also provide an expan...
Macrodystrophia lipomatosa (ML) is a rare congenital non-hereditary condition caused by an increase in all mesenchymal elements. We report a 14-year-old girl who presented to the Medical Outpatient Department, Kunhitharuvai Memorial Charitable Trust Medical College, Kozhikode, India, in 2017 with progressive enlargement of digits. An X-ray and T1-weighted magnetic resonance imaging scan showed enl...
BACKGROUND: Cervical spine injuries (CSI) are a major concern in young pediatric trauma patients. The consequences of missed injuries and difficulties...
There is growing appreciation that the success of digital health - whether digital tools, digital interventions or technology-based change strategies ...
Detection of abnormalities in wireless capsule endoscopy (WCE) images is a challenging task. Typically, these images suffer from low contrast, complex...
In the past decade, machine learning and artificial intelligence have made significant advancements in pattern analysis, including speech and natural ...
BACKGROUND: The combination of growth hormone (GH) and aromatase inhibitors (AI) improves linear growth in severely short adolescent boys; however, th...
It is evident through biology research that, biological neural network could be implemented through two means: by congenital heredity, or by posterior...
BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentati...
The cytokinesis-block micronucleus (CBMN) assay has become a fully-validated and standardized method for radiation biodosimetry. The assay is typicall...
Health care organizations are leveraging machine-learning techniques, such as artificial neural networks (ANN), to improve delivery of care at a reduc...
OBJECTIVE: The rapid proliferation of machine learning research using electronic health records to classify healthcare outcomes offers an opportunity ...
Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repeated procedural pain is associated with adverse ef...
BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is the 10th leading cause of death overall and the seco...
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have...
Annotation of foot-contact and foot-off events is the initial step in post-processing for most quantitative gait analysis workflows. If clean force pl...
BACKGROUND: Optimizing transplant candidates' priority for donor organs depends on the accurate assessment of post-transplant outcomes. Due to the com...
Polyphenols are known for their antimicrobial activity, whilst both polyphenols and the globular protein β-lactoglobulin (bLG) are suggested to have a...
Proactive detection of hemodynamic shock can prevent organ failure and save lives. Thermal imaging is a non-invasive, non-contact modality to capture ...
Recent studies have shown that growth differentiation factor 15 (GDF15), a member of the transforming growth factor-β (TGF-β)/bone morphogenetic prot...