Risk Management

Rapid MR examination with deep learning reconstruction improves diagnosis of complicated acute appendicitis.

OBJECTIVE: To evaluate the performance of a non-contrast rapid magnetic resonance imaging (MRI) protocol with deep learning reconstruction (DLR) for identifying complicated appendicitis in patients with acute appendicitis, compared to conventional MRI without DLR (non-DLR) and non-contrast computed tomography (NC-CT). METHODS: Sixty-two eligible patients were enrolled, of which 55 had a final diag...

Accuracy of Photon Dose Calculation on Photon-Counting Computed Tomography-A Comparison Study Based on Virtual Monoenergetic and Electron Density (Rho) Images for Pancreatic Cases.

PURPOSE: Photon-counting computed tomography (PCCT) offers versatile anatomic information because of better energy discrimination and higher spatial resolution than conventional energy-integrating computed tomography (CT). With its rapid applications in diagnostic imaging, its potential within radiation oncology remains largely unexplored. Successful radiation therapy (RT) relies on both high-qual...

The use of machine learning to predict pharmacological therapy in gestational diabetes: A scoping review.

AIMS: Early identification of pharmacological therapy for gestational diabetes mellitus (GDM), a common pregnancy complication, through machine learni...

Deep learning-based obstructive coronary artery disease prediction from myocardial perfusion SPECT.

PURPOSE: We aim to apply the deep learning (DL) technique to predict the gold-standard invasive coronary angiography (ICA) for coronary artery disease...

Artificial superintelligence alignment in healthcare.

The emergence of Artificial Superintelligence (ASI) in healthcare presents unprecedented opportunities for revolutionizing diagnostics, treatment plan...

Targetable atrial tachycardias after artificial intelligence-guided ablation of persistent atrial fibrillation.

BACKGROUND: The TAILORED-AF randomized trial demonstrated that artificial intelligence-guided ablation of spatiotemporal dispersion in addition to pul...

Deep learning and molecular dynamics reveal promising EZH2 inhibitors for epigenetic cancer targeting.

Fine-tuned REINVENT generative model integrated with structure- and ligand-based computational approaches was applied to identify novel EZH2 inhibitor...

Risk prediction models for adverse drug reactions in pediatrics: a scoping review.

INTRODUCTION: Predictive models play a critical role in enhancing medication safety in clinical practice. While multiple models for adverse drug react...

Deep learning approach for DCE-MRI parameter estimation: Evaluating signal intensity and concentration-time curve-based convolution-neural-networks.

Dynamic contrast-enhanced MRI (DCE-MRI) is common technique for assessing tissue perfusion and permeability in brain tumors (e.g., gliomas), using gen...

Artificial intelligence methods in gestational diabetes mellitus prediction: A systematic literature review.

Gestational diabetes mellitus (GDM) is the most common metabolic disorder in pregnancy, posing risks to both maternal and neonatal health. Artificial ...

Artificial Intelligence for CT and MRI Protocoling: A Meta-Analysis of Traditional Machine Learning, BERT, and Large Language Models.

Background: Examination protocoling is a resource-intensive task. Various artificial intelligence (AI) approaches have been investigated to automate t...

Multimodal deep learning for acute myocardial infarction detection from 12-lead electrocardiogram: a multi-centre study with cross-hospital validation.

AIMS: Acute myocardial infarction (AMI) remains a leading global cause of mortality, where timely diagnosis is critical to enable early intervention. ...

Biased minds, sensitive hearts: Divergent neural signatures of interpersonal sensitivity in atypical and non-atypical depression.

BACKGROUND: Atypical depression (AD) is a distinct subtype of depression, with interpersonal sensitivity as one of its core characteristics. However, ...

Real-world diagnostic performance of knee MRI protocols accelerated using simultaneous multi-slice acquisition and deep learning reconstruction.

OBJECTIVE: To assess whether accelerated knee MRI protocols using simultaneous multi-slice (SMS) and deep learning reconstruction (DLR) are non-inferi...

Reinforcement learning-based funnel control and privacy preservation for multi-agent systems with input dead-zone.

This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to in...

A Window Into Vulnerability and Support: At-Risk Mothers' Perinatal Experiences and Perspectives on mHealth-Based Care.

AIM: To examine the perinatal experiences of at-risk mothers and their engagement with mobile-health-based care. DESIGN: A qualitative descriptive stu...

Machine Learning to Predict Extranodal Extension in Head and Neck Squamous Cell Carcinoma: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the clinical utility of machine learning algorithms (MLAs) in diagnosing extra-nodal extension (ENE) using CT imaging in HNSCC....

Artificial Intelligence-Based Algorithms Improve Care of Patients with AAA.

BACKGROUND: Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial ...

Public Preferences for Emerging Cancer Screening Modalities: A Systematic Review of Discrete Choice Experiments.

BACKGROUND: Early detection of cancer reduces mortality and morbidity, but conventional screening methods often face challenges such as invasiveness, ...

A deep learning-enabled toolkit for the 3D segmentation of ventricular cardiomyocytes.

Segmentation of cardiomyocytes in microscopic 3D volumes is key to our understanding of cardiac (patho-)physiology; however, it poses substantial expe...