AIMC Topic: Aged
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Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.
Academic radiology
Feb 24, 2025
RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-...
Deep transfer learning radiomics for distinguishing sinonasal malignancies: a preliminary MRI study.
Future oncology (London, England)
Feb 24, 2025
PURPOSE: This study aimed to assess the diagnostic accuracy of combining MRI hand-crafted (HC) radiomics features with deep transfer learning (DTL) in identifying sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC), and non-Hodgki...
Deep learning to quantify the pace of brain aging in relation to neurocognitive changes.
Proceedings of the National Academy of Sciences of the United States of America
Feb 24, 2025
Brain age (BA), distinct from chronological age (CA), can be estimated from MRIs to evaluate neuroanatomic aging in cognitively normal (CN) individuals. BA, however, is a cross-sectional measure that summarizes cumulative neuroanatomic aging since bi...
SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.
International journal of molecular sciences
Feb 24, 2025
Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential...
Machine learning-based prediction model for patients with recurrent Staphylococcus aureus bacteremia.
BMC medical informatics and decision making
Feb 24, 2025
BACKGROUND: Staphylococcus aureus bacteremia (SAB) remains a significant contributor to both community-acquired and healthcare-associated bloodstream infections. SAB exhibits a high recurrence rate and mortality rate, leading to numerous clinical tre...
Natural language processing of electronic medical records identifies cardioprotective agents for anthracycline induced cardiotoxicity.
Scientific reports
Feb 24, 2025
In this retrospective observational study, we aimed to investigate the potential of natural language processing (NLP) for drug repositioning by analyzing the preventive effects of cardioprotective drugs against anthracycline-induced cardiotoxicity (A...
Calcium Channel Blockers
Hydroxymethylglutaryl-CoA Reductase Inhibitors
Angiotensin Receptor Antagonists
Angiotensin-Converting Enzyme Inhibitors
Cardiotonic Agents
Cardiotoxicity
Anthracyclines
Adrenergic beta-Antagonists
Adult
Male
Middle Aged
Drug Repositioning
Natural Language Processing
Retrospective Studies
Electronic Health Records
Female
Aged
Humans
Cognitive performance classification of older patients using machine learning and electronic medical records.
Scientific reports
Feb 24, 2025
Dementia rates are projected to increase significantly by 2050, posing considerable challenges for healthcare systems worldwide. Developing efficient diagnostic tools is critical, and machine learning (ML) algorithms have shown potential for improvin...
Validation of a machine learning model for indirect screening of suicidal ideation in the general population.
Scientific reports
Feb 24, 2025
Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide. However, suicide deaths are preventable with timely and evidence-based interventions, which are of...
Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on MGMT-methylation status, age and sex.
Acta neurochirurgica
Feb 24, 2025
PURPOSE: Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms...
Development and validation of predictive models for diabetic retinopathy using machine learning.
PloS one
Feb 24, 2025
OBJECTIVE: This study aimed to develop and compare machine learning models for predicting diabetic retinopathy (DR) using clinical and biochemical data, specifically logistic regression, random forest, XGBoost, and neural networks.
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