AIMC Topic: Infant

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On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.

BMC medical informatics and decision making
BACKGROUND: Care during pregnancy, childbirth and puerperium are fundamental to avoid pathologies for the mother and her baby. However, health issues can occur during this period, causing misfortunes, such as the death of the fetus or neonate. Predic...

Review of robot-assisted laparoscopic surgery in management of infant congenital urology: Advances and limitations in utilization and learning.

International journal of urology : official journal of the Japanese Urological Association
As robotic-assisted (RAL) surgery expanded to treat pediatric congenital disease, infant anatomy and physiology posed unique challenges that prompted adaptations to the technology and surgical technique, which are compiled and reviewed in this manusc...

Emergent color categorization in a neural network trained for object recognition.

eLife
Color is a prime example of categorical perception, yet it is unclear why and how color categories emerge. On the one hand, prelinguistic infants and several animals treat color categorically. On the other hand, recent modeling endeavors have success...

TwinEDA: a sustainable deep-learning approach for limb-position estimation in preterm infants' depth images.

Medical & biological engineering & computing
Early diagnosis of neurodevelopmental impairments in preterm infants is currently based on the visual analysis of newborns' motion patterns by trained operators. To help automatize this time-consuming and qualitative procedure, we propose a sustainab...

Robot-assisted laparoscopic repair of cesarean scar defect: a systematic review of clinical evidence.

Journal of robotic surgery
We aim to assess the available evidence concerning the robot-assisted repair of cesarean scar defect. A systematic PubMed and Scopus search was conducted. All databases were assessed up to May 23, 2022. Studies reporting data on robot-assisted repair...

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...