AI Medical Compendium Topic:
Child, Preschool

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Age estimates from brain magnetic resonance images of children younger than two years of age using deep learning.

Magnetic resonance imaging
The accuracy of brain age estimates from magnetic resonance (MR) images has improved with the advent of deep learning artificial intelligence (AI) models. However, most previous studies on predicting age emphasized aging from childhood to adulthood a...

How can the accuracy of SEEG be increased?-an analysis of the accuracy of multilobe-spanning SEEG electrodes based on a frameless stereotactic robot-assisted system.

Annals of palliative medicine
BACKGROUND: A frameless stereotactic robot-assisted system allows stereoelectroencephalography (SEEG) electrodes to span multiple lobes. As the angularity and length are increased, maintaining accuracy of the electrodes becomes more challenging. The ...

Reducing negative emotions in children using social robots: systematic review.

Archives of disease in childhood
BACKGROUND: For many children, visiting the hospital can lead to a state of increased anxiety. Social robots are being explored as a possible tool to reduce anxiety and distress in children attending a clinical or hospital environment. Social robots ...

Accurate age classification using manual method and deep convolutional neural network based on orthopantomogram images.

International journal of legal medicine
Age estimation is an important challenge in many fields, including immigrant identification, legal requirements, and clinical treatments. Deep learning techniques have been applied for age estimation recently but lacking performance comparison betwee...

Development of a Machine Learning Model Using Electronic Health Record Data to Identify Antibiotic Use Among Hospitalized Patients.

JAMA network open
IMPORTANCE: Comparisons of antimicrobial use among hospitals are difficult to interpret owing to variations in patient case mix. Risk-adjustment strategies incorporating larger numbers of variables haves been proposed as a method to improve compariso...

Machine Learning Assessment of Early Life Factors Predicting Suicide Attempt in Adolescence or Young Adulthood.

JAMA network open
IMPORTANCE: Although longitudinal studies have reported associations between early life factors (ie, in-utero/perinatal/infancy) and long-term suicidal behavior, they have concentrated on 1 or few selected factors, and established associations, but d...

Personalized prediction of early childhood asthma persistence: A machine learning approach.

PloS one
Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to identify individuals whose symptoms will persist. Our objective was to develop machine learning models...

External validation of automated focal cortical dysplasia detection using morphometric analysis.

Epilepsia
OBJECTIVE: Focal cortical dysplasias (FCDs) are a common cause of drug-resistant focal epilepsy but frequently remain undetected by conventional magnetic resonance imaging (MRI) assessment. The visual detection can be facilitated by morphometric anal...

CKLF and IL1B transcript levels at diagnosis are predictive of relapse in children with pre-B-cell acute lymphoblastic leukaemia.

British journal of haematology
Disease relapse is the greatest cause of treatment failure in paediatric B-cell acute lymphoblastic leukaemia (B-ALL). Current risk stratifications fail to capture all patients at risk of relapse. Herein, we used a machine-learning approach to identi...

Primary vs redo robotic pyeloplasty: A comparison of outcomes.

Journal of pediatric urology
INTRODUCTION: Robot-assisted laparoscopic pyeloplasty (RALP) is a safe and efficacious option for repair of UPJO. We hypothesize that redo-RALP is technically more difficult but has comparable outcomes to primary RALP.