AIMC Topic: Child

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Interoperable Models for Identifying Critically Ill Children at Risk of Neurologic Morbidity.

JAMA network open
IMPORTANCE: Decreasing mortality in the field of pediatric critical care medicine has shifted practicing clinicians' attention to preserving patients' neurodevelopmental potential as a main objective. Earlier identification of critically ill children...

Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: This study aimed to develop a deep-learning framework to generate multi-organ masks from CT images in adult and pediatric patients.

Navigating the future of pediatric cardiovascular surgery: Insights and innovation powered by Chat Generative Pre-Trained Transformer (ChatGPT).

The Journal of thoracic and cardiovascular surgery
INTRODUCTION: Interdisciplinary consultations are essential to decision-making for patients with congenital heart disease. The integration of artificial intelligence (AI) and natural language processing into medical practice is rapidly accelerating, ...

Normative values for lung, bronchial sizes, and bronchus-artery ratios in chest CT scans: from infancy into young adulthood.

European radiology
OBJECTIVE: To estimate the developmental trends of quantitative parameters obtained from chest computed tomography (CT) and to provide normative values on dimensions of bronchi and arteries, as well as bronchus-artery (BA) ratios from preschool age t...

Artificial intelligence system for automatic tooth detection and numbering in the mixed dentition in CBCT.

European journal of paediatric dentistry
AIM: To evaluate the effectiveness and accuracy of artificial intelligence (AI) by automating tooth segmentation in CBCT volumes of paediatric patients with mixed dentition, using nnU-Netv2 algorithm.

Integrating machine learning for treatment decisions in anterior open bite orthodontic cases: A retrospective study.

Journal of the World federation of orthodontists
INTRODUCTION: This article explores the integration of machine learning (ML) algorithms to aid in treatment planning and extraction decisions for anterior open bite cases, leveraging demographic, clinical, and radiographic data to predict treatment o...

AI-based non-invasive imaging technologies for early autism spectrum disorder diagnosis: A short review and future directions.

Artificial intelligence in medicine
Autism Spectrum Disorder (ASD) is a neurological condition, with recent statistics from the CDC indicating a rising prevalence of ASD diagnoses among infants and children. This trend emphasizes the critical importance of early detection, as timely di...

Automated strabismus detection and classification using deep learning analysis of facial images.

Scientific reports
Strabismus, or eye misalignment, is a common condition affecting individuals of all ages. Early detection and accurate classification are essential for proper treatment and avoiding long-term complications. This research presents a new deep-learning-...

Evaluating waist-to-hip ratio in youth using frequency-modulated continuous wave radar and machine learning.

Scientific reports
Waist-to-hip ratio (WHR) is an essential predictor of cardiometabolic diseases, but traditional tape-based WHR measurements in children and adolescents can cause discomfort due to direct contact and are prone to measurer variation. This study aimed t...

Multiparametric MRI-based machine learning system of molecular subgroups and prognosis in medulloblastoma.

European radiology
OBJECTIVES: We aimed to use artificial intelligence to accurately identify molecular subgroups of medulloblastoma (MB), predict clinical outcomes, and incorporate deep learning-based imaging features into the risk stratification.