AIMC Topic: Child

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Three-dimensional automated segmentation of adolescent idiopathic scoliosis on computed tomography driven by deep learning: A retrospective study.

Medicine
Accurate vertebrae segmentation is crucial for modern surgical technologies, and deep learning networks provide valuable tools for this task. This study explores the application of advanced deep learning-based methods for segmenting vertebrae in comp...

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

BMC medical imaging
BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. ...

Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study.

Scientific reports
Tourette syndrome is a relatively prevalent neurological condition, particularly among children, characterized by sudden, involuntary, repetitive movements or vocalizations. Contemporary diagnostic approaches for Tourette syndrome (TS) primarily rely...

Explainable deep learning for age and gender estimation in dental CBCT scans using attention mechanisms and multi task learning.

Scientific reports
Accurate and interpretable age estimation and gender classification are essential in forensic and clinical diagnostics, particularly when using high-dimensional medical imaging data such as Cone Beam Computed Tomography (CBCT). Traditional CBCT-based...

Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis.

BMC pediatrics
OBJECTIVE: To develop machine learning (ML) models incorporating explanatory cardiac magnetic resonance (CMR) parameters for predicting the prognosis of myocarditis in pediatric patients.

What makes a 'good' decision with artificial intelligence? A grounded theory study in paediatric care.

BMJ evidence-based medicine
OBJECTIVE: To develop a framework for good clinical decision-making using machine learning (ML) models for interventional, patient-level decisions.

Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.

Scientific reports
Osteosarcoma, an aggressive bone malignancy predominantly affecting children and adolescents, is characterized by a poor prognosis and high mortality rates. The development of reliable prognostic tools is critical for advancing personalized treatment...

Artificial intelligence in pediatric dental trauma: do artificial intelligence chatbots address parental concerns effectively?

BMC oral health
BACKGROUND: This study focused on two Artificial Intelligence chatbots, ChatGPT 3.5 and Google Gemini, as the primary tools for answering questions related to traumatic dental injuries. The aim of this study to evaluate the reliability, understandabi...

Artificial intelligence-guided distal radius fracture detection on plain radiographs in comparison with human raters.

Journal of orthopaedic surgery and research
BACKGROUND: The aim of this study was to compare the performance of artificial intelligence (AI) in detecting distal radius fractures (DRFs) on plain radiographs with the performance of human raters.

Metabolomics and machine learning identify urine metabolic characteristics and potential biomarkers for severe Mycoplasma pneumoniae pneumonia.

Scientific reports
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarker...