AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Child

Showing 81 to 90 of 2951 articles

Clear Filters

O blood usage trends in the pediatric population 2015-2019: A multi-institutional analysis.

Transfusion
BACKGROUND: In 2019, AABB released the bulletin "Recommendations on the Use of Group O Red Blood Cells" in which the recommendations about pediatric and neonatal blood transfusions were limited. Eight U.S. pediatric hospitals sought to determine tren...

Prediction of Hypertension in the Pediatric Population Using Machine Learning and Transfer Learning: A Multicentric Analysis of the SAYCARE Study.

International journal of public health
OBJECTIVE: To develop a machine learning (ML) model utilizing transfer learning (TL) techniques to predict hypertension in children and adolescents across South America.

Machine learning in lymphocyte and immune biomarker analysis for childhood thyroid diseases in China.

BMC pediatrics
OBJECTIVE: This study aims to characterize and analyze the expression of representative biomarkers like lymphocytes and immune subsets in children with thyroid disorders. It also intends to develop and evaluate a machine learning model to predict if ...

Machine learning-based risk prediction model for pertussis in children: a multicenter retrospective study.

BMC infectious diseases
BACKGROUND: Pertussis is a highly contagious respiratory disease. Even though vaccination has reduced the incidence, cases have resurfaced in certain regions due to immune escape and waning vaccine efficacy. Identifying high-risk patients to mitigate...

Deep learning based quantitative cervical vertebral maturation analysis.

Head & face medicine
OBJECTIVES: This study aimed to enhance clinical diagnostics for quantitative cervical vertebral maturation (QCVM) staging with precise landmark localization. Existing methods are often subjective and time-consuming, while deep learning alternatives ...

Integrating bioinformatics and machine learning for comprehensive analysis and validation of diagnostic biomarkers and immune cell infiltration characteristics in pediatric septic shock.

Scientific reports
This study aims to predict and diagnose pediatric septic shock through the screening of immune infiltration-related biomarkers. Three gene expression datasets were accessible from the Gene Expression Omnibus repository. The differentially expressed g...

Machine learning-driven development of a stratified CES-D screening system: optimizing depression assessment through adaptive item selection.

BMC psychiatry
OBJECTIVE: To develop a stratified screening tool through machine learning approaches for the Center for Epidemiologic Studies Depression Scale (CES-D-20) while maintaining diagnostic accuracy, addressing the efficiency limitations in large-scale app...

Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and Attention-Deficit/Hyperactivity Disorder With Psychological Test Reports.

Journal of Korean medical science
BACKGROUND: Psychological test reports are essential in assessing intellectual functioning, aiding in diagnosing and treating intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD). However, these reports can have several pr...

Development and validation of pan-cancer lesion segmentation AI-model for whole-body 18F-FDG PET/CT in diverse clinical cohorts.

Computers in biology and medicine
BACKGROUND: This study develops a deep learning-based automated lesion segmentation model for whole-body 3DF-fluorodeoxyglucose (FDG)-Position emission tomography (PET) with computed tomography (CT) images agnostic to disease location and site.

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

The Lancet. Digital health
BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart disease. Although artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis is predictive of ...