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Child

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Handling missing data and measurement error for early-onset myopia risk prediction models.

BMC medical research methodology
BACKGROUND: Early identification of children at high risk of developing myopia is essential to prevent myopia progression by introducing timely interventions. However, missing data and measurement error (ME) are common challenges in risk prediction m...

Prediction of patient cooperation before orthodontic treatment: Handwriting and artificial intelligence.

Journal of the World federation of orthodontists
BACKGROUND: The purpose of this study was to compare the success of various convolutional neural network (CNN) models trained with handwriting samples in predicting patient cooperation.

Evaluation of tooth development stages with deep learning-based artificial intelligence algorithm.

BMC oral health
BACKGROUND: This study aims to evaluate the performance of a deep learning system for the evaluation of tooth development stages on images obtained from panoramic radiographs from child patients.

Lateral cephalometric parameters among Arab skeletal classes II and III patients and applying machine learning models.

Clinical oral investigations
BACKGROUND: The World Health Organization considers malocclusion one of the most essential oral health problems. This disease influences various aspects of patients' health and well-being. Therefore, making it easier and more accurate to understand a...

High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.

Cancer science
Primary malignant bone tumors, such as osteosarcoma, significantly affect the pediatric and young adult populations, necessitating early diagnosis for effective treatment. This study developed a high-performance artificial intelligence (AI) model to ...

An international study presenting a federated learning AI platform for pediatric brain tumors.

Nature communications
While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across h...

Three contrasts in 3 min: Rapid, high-resolution, and bone-selective UTE MRI for craniofacial imaging with automated deep-learning skull segmentation.

Magnetic resonance in medicine
PURPOSE: Ultrashort echo time (UTE) MRI can be a radiation-free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone-specific MR imaging is limited by long scan times, relatively low spatial resolution, and a tim...

Machine learning in paediatric haematological malignancies: a systematic review of prognosis, toxicity and treatment response models.

Pediatric research
BACKGROUND: Machine Learning (ML) has demonstrated potential in enhancing care in adult oncology. However, its application in paediatric haematological malignancies is still emerging, necessitating a comprehensive review of its capabilities and limit...

Deep learning for efficient reconstruction of highly accelerated 3D FLAIR MRI in neurological deficits.

Magma (New York, N.Y.)
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...

Machine learning-derived phenotypic trajectories of asthma and allergy in children and adolescents: protocol for a systematic review.

BMJ open
INTRODUCTION: Development of asthma and allergies in childhood/adolescence commonly follows a sequential progression termed the 'atopic march'. Recent reports indicate, however, that these diseases are composed of multiple distinct phenotypes, with p...