AIMC Topic: Aged
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Ultra-fast single-sequence magnetic resonance imaging (MRI) for lower back pain: diagnostic performance of a deep learning T2-Dixon pprotocol.
Clinical radiology
Jun 11, 2025
BACKGROUND: Conventional magnetic resonance imaging (MRI) protocols for lower back pain require multiple sequences and long acquisition times, challenging healthcare systems amid rising demand for lumbar spine imaging.
Use of machine learning for real-time antibiotic treatment adjustment in high-risk patients with CRGNB infection.
Computer methods and programs in biomedicine
Jun 11, 2025
BACKGROUND: Infections caused by carbapenem resistant gram-negative bacilli (CRGNB) are associated with high mortality and pose a great challenge for clinical treatment. We aim to identify patients at high risk for CRGNB as early as possible and aler...
A machine learning approach for personalized breast radiation dosimetry in CT: Integrating radiomics and deep neural networks.
European journal of radiology
Jun 11, 2025
PURPOSE: To develop a machine learning-based workflow for patient-specific breast radiation dosimetry in CT.
Patient perspectives on AI in radiology: Insights from the United Arab Emirates.
Clinical imaging
Jun 11, 2025
RATIONALE AND OBJECTIVES: Artificial intelligence (AI) enhances diagnostic accuracy, efficiency, and patient outcomes in radiology. Patient acceptance is essential for successful integration. This study examines patient perspectives on AI in radiolog...
A plasma metabolome-derived model predicts severe liver outcomes of nonalcoholic fatty liver disease in the UK Biobank.
Diabetes, obesity & metabolism
Jun 11, 2025
AIMS: Severe liver disease (SLD) in nonalcoholic fatty liver disease (NAFLD) is often diagnosed late due to the long asymptomatic period of progressive fibrosis. We aimed to identify metabolomic profiles associated with SLD and develop a predictive m...
Evaluation of semi-automated versus fully automated technologies for computed tomography scalable body composition analyses in patients with severe acute respiratory syndrome Coronavirus-2.
Clinical nutrition ESPEN
Jun 11, 2025
RATIONALE AND OBJECTIVES: Fully automated, artificial intelligence (AI) -based software has recently become available for scalable body composition analysis. Prior to broad application in the clinical arena, validation studies are needed. Our goal wa...
Identification of neurological text markers associated with risk of stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Jun 11, 2025
BACKGROUND: Delayed or missed stroke diagnosis is associated with poor outcomes. We utilized natural language processing of notes from non-neurological emergency department (ED) encounters to identify text phrases indicating stroke presentations that...
Academic Medical Centers
Male
Predictive Value of Tests
Middle Aged
Female
Humans
Natural Language Processing
Reproducibility of Results
Risk Assessment
Emergency Service, Hospital
Prognosis
Risk Factors
Time Factors
Retrospective Studies
Stroke
Aged
Electronic Health Records
Data Mining
Aged, 80 and over
Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning.
Computers in biology and medicine
Jun 11, 2025
Prostate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. W...
Identifying patients at risk of increased health utilization following lumbar spine surgery.
Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Jun 11, 2025
BACKGROUND: Adequate preoperative identification of patients at risk of significant healthcare utilization after surgery could help guide preoperative decision-making as well as postoperative patient management. While several studies have proposed me...
Comparison of Multimodal Deep Learning Approaches for Predicting Clinical Deterioration in Ward Patients: Observational Cohort Study.
Journal of medical Internet research
Jun 11, 2025
BACKGROUND: Implementing machine learning models to identify clinical deterioration in the wards is associated with decreased morbidity and mortality. However, these models have high false positive rates and only use structured data.
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