AI Medical Compendium Topic

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Child, Preschool

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Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia.

British journal of haematology
In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the...

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

Pediatric radiology
BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice.

Survival trend and outcome prediction for pediatric Hodgkin and non-Hodgkin lymphomas based on machine learning.

Clinical and experimental medicine
Pediatric Hodgkin and non-Hodgkin lymphomas differ from adult cases in biology and management, yet there is a lack of survival analysis tailored to pediatric lymphoma. We analyzed lymphoma data from 1975 to 2018, comparing survival trends between 7,8...

Cardiac patients' surgery outcome and associated factors in Ethiopia: application of machine learning.

BMC pediatrics
INTRODUCTION: Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed natio...

Machine learning analysis with population data for prepregnancy and perinatal risk factors for the neurodevelopmental delay of offspring.

Scientific reports
Neurodevelopmental disorders (NDD) in offspring are associated with a complex combination of pre-and postnatal factors. This study uses machine learning and population data to evaluate the association between prepregnancy or perinatal risk factors an...

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Archives of toxicology
Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase o...

Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.

JDR clinical and translational research
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.

RDLR: A Robust Deep Learning-Based Image Registration Method for Pediatric Retinal Images.

Journal of imaging informatics in medicine
Retinal diseases stand as a primary cause of childhood blindness. Analyzing the progression of these diseases requires close attention to lesion morphology and spatial information. Standard image registration methods fail to accurately reconstruct pe...

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Scientific reports
Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scar...

Prediction Models for Intravenous Immunoglobulin Non-Responders of Kawasaki Disease Using Machine Learning.

Clinical drug investigation
BACKGROUND AND OBJECTIVE: Intravenous immunoglobulin (IVIG) is a prominent therapeutic agent for Kawasaki disease (KD) that significantly reduces the incidence of coronary artery anomalies. Various methodologies, including machine learning, have been...