Multimodality imaging is an emerging research topic in neuro-oncology for its potential of being able to demonstrate tumours in a more comprehensive manner. Diffusion-weighted magnetic resonance imaging (dMRI) and proton magnetic resonance spectrosco...
OBJECTIVE: This study aimed to improve treatment effectiveness in childhood-onset systemic lupus erythematosus (cSLE) by developing machine learning algorithms integrated with pharmacokinetic parameters to predict individualized tacrolimus dosing for...
Ecotoxicology and environmental safety
Sep 1, 2025
BACKGROUND: Polycyclic aromatic hydrocarbons (PAHs), widely emitted through industrial processes and vehicular exhaust, are recognized environmental carcinogens. Although PAH exposure has been linked to various malignancies, the specific molecular me...
European journal of clinical pharmacology
Sep 1, 2025
PURPOSE: This study develops and compares population pharmacokinetics (PopPK) models and machine learning methods, including neural networks, to predict steady-state trough concentrations in pediatric patients and provide improved dosing recommendati...
Bronchiolitis obliterans syndrome (BOS) is a severe pulmonary complication following allogeneic hematopoietic stem cell transplantation (allo-HSCT), with early prediction being crucial. While pulmonary function tests (PFTs) are fundamental for BOS as...
Overtraining syndrome (OTS) poses a critical challenge in youth soccer, particularly during periods of rapid physiological maturation combined with high training demands. This study aimed to develop and validate a multidimensional prediction model fo...
BACKGROUND: The evolution in peri-ictal period (from pre-ictal to ictal phase) of seizures contains abundant epileptogenic information, which aids in exploring the mechanism of seizures and localizing the epileptogenic zone (EZ). This study aims to i...
This study focuses on the binary classification of pediatric epilepsy seizure types as focal or generalized using Turkish electroencephalography (EEG) reports, leveraging natural language processing (NLP) and machine learning methodologies. A novel d...
OBJECTIVE: This study aimed to evaluate the diagnostic performance of an artificial intelligence (AI)-based platform (Diagnocat) in detecting periapical radiolucencies (PARLs) in cone-beam computed tomography (CBCT) scans of molars. Specifically, we ...
RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia.
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