RATIONALE AND OBJECTIVES: To construct and validate an interpretable machine learning (ML) radiomics model derived from multiparametric magnetic resonance imaging (MRI) images to differentiate between luminal and non-luminal breast cancer (BC) subtyp...
BACKGROUND: Artificial intelligence (AI) integration in nursing simulation education is growing, yet understanding its implementation across simulation phases remains limited.
Neonatal seizures are a common medical emergency, necessitating prompt treatment. The most common etiologies include hypoxic-ischemic encephalopathy, ischemic stroke, and intracranial hemorrhage, with numerous other uncommon etiologies. Accurate diag...
International journal of surgery (London, England)
Apr 1, 2025
Right ventricular dysfunction following surgical correction of tetralogy of Fallot (TOF) remains a major determinant of long-term morbidity and mortality in survivors. Despite advancements in surgical techniques, residual anatomical abnormalities - i...
International journal of surgery (London, England)
Apr 1, 2025
BACKGROUND: Machine Learning (ML) is increasingly being adopted in biomedical research, however, its potential for outcome prediction in visceral surgery remains uncertain. This study compares the potential of ML methods for preoperative 90-day morta...
INTRODUCTION: Overactive bladder (OAB) is a common urological condition with increasing prevalence, especially in an aging population. Diagnosing and treating OAB can be challenging. While urodynamic study (UDS) is useful to confirm involuntary detru...
BACKGROUND: As auto-segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. One notable example is the use of adult-trained commercial software for the contouring of organs at ...
International journal of obesity (2005)
Apr 1, 2025
OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical imped...
BACKGROUND: Individualized treatment decisions for multiple myeloma (MM) patients require accurate risk stratification that accounts for patient-specific consequences of cytogenetic abnormalities on disease progression.
OBJECTIVE: This study aims to report human performance in the detection of Focal Cortical Dysplasias (FCDs) using an openly available dataset. Additionally, it defines a subset of this data as a "difficult" test set to establish a public baseline ben...
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