AIMC Topic: Child

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Artificial intelligence to improve rehabilitation care for children with developmental conditions: Some ethical considerations.

Developmental medicine and child neurology
This commentary is on the original article by Greve et al. on pages 100‐106 of this issue.

Multi-structure bone segmentation in pediatric MR images with combined regularization from shape priors and adversarial network.

Artificial intelligence in medicine
Morphological and diagnostic evaluation of pediatric musculoskeletal system is crucial in clinical practice. However, most segmentation models do not perform well on scarce pediatric imaging data. We propose a new pre-trained regularized convolutiona...

A Hybrid Expert System for Individualized Quantification of Electrical Status Epilepticus During Sleep Using Biogeography-Based Optimization.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Electrical status epilepticus during sleep (ESES) is an epileptic encephalopathy in children with complex clinical manifestations. It is accompanied by specific electroencephalography (EEG) patterns of continuous spike and slow-waves. Quantifying suc...

Auxiliary Pneumonia Classification Algorithm Based on Pruning Compression.

Computational and mathematical methods in medicine
Pneumonia infection is the leading cause of death in young children. The commonly used pneumonia detection method is that doctors diagnose through chest X-ray, and external factors easily interfere with the results. Assisting doctors in diagnosing pn...

Barriers to Use Artificial Intelligence Methodologies in Health Technology Assessment in Central and East European Countries.

Frontiers in public health
The aim of this paper is to identify the barriers that are specifically relevant to the use of Artificial Intelligence (AI)-based evidence in Central and Eastern European (CEE) Health Technology Assessment (HTA) systems. The study relied on two main ...

Deep learning-based identification of mesiodens using automatic maxillary anterior region estimation in panoramic radiography of children.

Dento maxillo facial radiology
OBJECTIVES: The purpose of this study is to develop and evaluate the performance of a model that automatically sets a region of interest (ROI) and diagnoses mesiodens in panoramic radiographs of growing children using deep learning technology.

Deep Learning Dual Neural Networks in the Construction of Learning Models for Online Courses in Piano Education.

Computational intelligence and neuroscience
The use of deep learning (DL) and artificial intelligence (AI) in teaching children piano lessons promotes modern piano instruction and enhances the overall quality of education. To begin, a more thorough explanation of the teaching environment and t...

Compact pediatric cardiac magnetic resonance imaging protocols.

Pediatric radiology
Cardiac MRI is in many respects an ideal modality for pediatric cardiovascular imaging, enabling a complete noninvasive assessment of anatomy, morphology, function and flow in one radiation-free and potentially non-contrast exam. Nonetheless, traditi...

Pedicle Screw Placement in Adolescent Idiopathic Scoliosis: A Comparison between Robotics Coupled with Navigation versus the Freehand Technique.

Sensors (Basel, Switzerland)
(1) Background: Robotics coupled with navigation (RAN) is a modern surgical platform shown to increase screw placement accuracy during pediatric scoliosis surgery. Our institution uses a technique which combines the RAN platform for apical pedicle sc...

Deep learning accurately classifies elbow joint effusion in adult and pediatric radiographs.

Scientific reports
Joint effusion due to elbow fractures are common among adults and children. Radiography is the most commonly used imaging procedure to diagnose elbow injuries. The purpose of the study was to investigate the diagnostic accuracy of deep convolutional ...