AIMC Topic: Child, Preschool

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YOLO11m-cls applied to sex and age classification based on the radiographic analysis of the nasal aperture.

Scientific reports
Deep learning tools based on computer vision have emerged as alternative methods for assessing radiographic image patterns. These approaches have been explored for various forensic applications, including sex and age estimation. This study aimed to e...

Impact of contrast enhancement boost and super-resolution deep learning reconstruction on pediatric congenital heart disease CTA scans: ultra-low contrast dose.

BMC medical imaging
OBJECTIVE: To evaluate the feasibility of using contrast enhancement boost (CE-Boost) combined with super-resolution deep learning reconstruction (SR-DLR) to reduce contrast agent dosage in pediatric patients with congenital heart disease (CHD).

MRI multi-sequence deep learning integration with clinical profiles for pediatric viral encephalitis diagnosis.

Scientific reports
Pediatric viral encephalitis is an acute central nervous system infection caused by various viruses, with diverse clinical manifestations and challenges in early diagnosis. The traditional diagnostic methods lack sufficient sensitivity and specificit...

Performance of the pediatric index of mortality (PIM-3) in a Moroccan PICU: challenges in resource-limited settings.

European journal of pediatrics
UNLABELLED: Prognostic scores such as the Pediatric Index of Mortality (PIM-3) are widely used to estimate mortality risk in PICUs, yet their performance in low- and middle-income countries (LMICs) remains uncertain. We aimed to evaluate the predicti...

Large language model as a clinical decision support tool in the initial management of critically ill children: a pilot evaluation.

European journal of pediatrics
UNLABELLED: Large language models (LLMs) like ChatGPT are being explored as clinical decision support tools, but their reliability in pediatric acute care remains uncertain. This pilot study assessed ChatGPT-4.0's performance in the early management ...

Machine learning prediction of mortality in pediatric fungemia using the Candida score.

Scientific reports
Pediatric fungemia in pediatric intensive care units (PICUs) carries high mortality. We evaluated whether the Candida Score, combined with clinical variables, predicts mortality after diagnosis using a prespecified multivariable logistic regression (...

Integrating machine learning and time-to-event models to explain and predict risk of hospitalization due to dengue in Colombia.

Scientific reports
Arboviral diseases such as dengue pose major public health challenges in endemic regions, notably in Norte de Santander (Colombia), where they place substantial pressure on healthcare services. We analyzed 8,814 confirmed dengue cases reported to the...

An exploratory machine learning study on paediatric abdominal pain phenotyping and prediction.

PloS one
BACKGROUND: The exact mechanisms underlying paediatric abdominal pain (AP) remain unclear due to patient heterogeneity. This preliminary study aimed to identify AP phenotypes and develop predictive models to explore associated factors, with the goal ...

Artificial intelligence-based method for detecting wrist fractures in children.

Scientific reports
Pediatric wrist fractures are common skeletal injuries in clinical practice; however, due to the ongoing development of children's bones, fracture characteristics are complex and often prone to misdiagnosis or missed diagnosis. Moreover, traditional ...

Using a coloring activity to identify children's development of visual-motor integration: an application of artificial intelligence.

Annals of medicine
AIM: Visual-motor integration (VMI) is an important indicator in children with learning disabilities. We aimed to use performance in a coloring activity to identify children's VMI developmental status.