AI Medical Compendium Topic

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Infant

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Quantifying the Severity of Metopic Craniosynostosis Using Unsupervised Machine Learning.

Plastic and reconstructive surgery
BACKGROUND: Quantifying the severity of head shape deformity and establishing a threshold for operative intervention remains challenging in patients with metopic craniosynostosis (MCS). This study combines three-dimensional skull shape analysis with ...

Effect of AI-assisted software on inter- and intra-observer variability for the X-ray bone age assessment of preschool children.

BMC pediatrics
BACKGROUND: With the rapid development of deep learning algorithms and the rapid improvement of computer hardware in the past few years, AI-assisted diagnosis software for bone age has achieved good diagnostic performance. The purpose of this study w...

Novel approaches to capturing and using continuous cardiorespiratory physiological data in hospitalized children.

Pediatric research
Continuous cardiorespiratory physiological monitoring is a cornerstone of care in hospitalized children. The data generated by monitoring devices coupled with machine learning could transform the way we provide care. This scoping review summarizes ex...

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Biological psychiatry
Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Neverth...

Robotic ureteral reimplantation and uretero-ureterostomy treating the ureterovesical junction pathologies in children: technical considerations and preliminary results.

Journal of robotic surgery
Robot-assisted laparoscopic extravesical ureteral reimplantation (RALUR) and robotic ureteroureterostomy (RUU) are two mini-invasive surgical techniques that have begun to be performed in pediatric urology in recent years. RALUR has been employed esp...

The application of artificial intelligence to support biliary atresia screening by ultrasound images: A study based on deep learning models.

PloS one
PURPOSE: Early confirmation or ruling out biliary atresia (BA) is essential for infants with delayed onset of jaundice. In the current practice, percutaneous liver biopsy and intraoperative cholangiography (IOC) remain the golden standards for diagno...

Semi-supervised body parsing and pose estimation for enhancing infant general movement assessment.

Medical image analysis
General movement assessment (GMA) of infant movement videos (IMVs) is an effective method for early detection of cerebral palsy (CP) in infants. We demonstrate in this paper that end-to-end trainable neural networks for image sequence recognition can...

Deep Learning and 5G and Beyond for Child Drowning Prevention in Swimming Pools.

Sensors (Basel, Switzerland)
Drowning is a major health issue worldwide. The World Health Organization's global report on drowning states that the highest rates of drowning deaths occur among children aged 1-4 years, followed by children aged 5-9 years. Young children can drown ...

Laparoscopic versus robot-assisted pyeloplasty in infants and young children.

Asian journal of surgery
OBJECTIVE: To compare the characteristics of conventional laparoscopic pyeloplasty (LP) and robotic-assisted laparoscopic pyeloplasty (RALP) in infants and young children with ureteropelvic junction obstruction (UPJO).