AIMC Topic: Infant

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Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms.

Scientific reports
The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of panoramic radiographs based on age, and to estimate the age group of people with permanent dentition in a non-invasive, comprehensive, and accurate manner ...

Robotic Surgery in Pediatric Urology: A Critical Appraisal of the GECI and SIVI Consensus of European Experts.

Journal of laparoendoscopic & advanced surgical techniques. Part A
This study aimed to create a consensus statement on the indications, applications, and limitations of robotics in pediatric urology. After a panel and interactive discussion focused on pediatric robotics, a televoting with 10 questions was administ...

Robotic partial nephrectomy in the pediatric population: Cumulative experience at a large pediatric hospital.

Journal of pediatric urology
INTRODUCTION: Robotic partial nephrectomy is a complex minimally invasive procedure that addresses the intricate anatomy of renal masses while maximizing preservation of renal function. However, while common in adults, the evolution toward these mini...

Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.

JAMA network open
IMPORTANCE: Early identification of cerebral palsy (CP) is important for early intervention, yet expert-based assessments do not permit widespread use, and conventional machine learning alternatives lack validity.

Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores.

IEEE transactions on medical imaging
Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering ...

Added value of an artificial intelligence solution for fracture detection in the radiologist's daily trauma emergencies workflow.

Diagnostic and interventional imaging
PURPOSE: The main objective of this study was to compare radiologists' performance without and with artificial intelligence (AI) assistance for the detection of bone fractures from trauma emergencies.

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities.

Journal of biomedical informatics
Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed eva...

Explainable deep learning algorithm for distinguishing incomplete Kawasaki disease by coronary artery lesions on echocardiographic imaging.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Incomplete Kawasaki disease (KD) has often been misdiagnosed due to a lack of the clinical manifestations of classic KD. However, it is associated with a markedly higher prevalence of coronary artery lesions. Identifying cor...

Heart age estimated using explainable advanced electrocardiography.

Scientific reports
Electrocardiographic (ECG) Heart Age conveying cardiovascular risk has been estimated by both Bayesian and artificial intelligence approaches. We hypothesised that explainable measures from the 10-s 12-lead ECG could successfully predict Bayesian 5-m...

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review.

Sensors (Basel, Switzerland)
Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential ...