AIMC Journal:
Radiology

Showing 251 to 260 of 374 articles

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Radiology
Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Methods Four deidentified HIPAA-compliant datasets were used in this study that were exempted from revi...

Feasibility of Antegrade Contrast-enhanced US Nephrostograms to Evaluate Ureteral Patency.

Radiology
Purpose To demonstrate the feasibility of contrast material-enhanced ulrasonographic (US) nephrostograms to assess ureteral patency after percutaneous nephrolithotomy (PCNL) in this proof-of-concept study. Materials and Methods For this HIPAA-complia...

A generic support vector machine model for preoperative glioma survival associations.

Radiology
PURPOSE: To develop a generic support vector machine (SVM) model by using magnetic resonance (MR) imaging-based blood volume distribution data for preoperative glioma survival associations and to prospectively evaluate the diagnostic effectiveness of...

Adaptive Breast MRI Scanning Using AI.

Radiology
Background MRI protocols typically involve many imaging sequences and often require too much time. Purpose To simulate artificial intelligence (AI)-directed stratified scanning for screening breast MRI with various triage thresholds and evaluate its ...

Pitfalls and Best Practices in Evaluation of AI Algorithmic Biases in Radiology.

Radiology
Despite growing awareness of problems with fairness in artificial intelligence (AI) models in radiology, evaluation of algorithmic biases, or AI biases, remains challenging due to various complexities. These include incomplete reporting of demographi...

Diagnostic Accuracy of On-Premise Automated Coronary CT Angiography Analysis Based on Coronary Artery Disease Reporting and Data System 2.0.

Radiology
Background Chest pain is a leading cause of outpatient and emergency department visits; advancements in artificial intelligence (AI) could improve coronary CT angiography (CCTA) workflows for these patients. Purpose To evaluate the performance of an ...

Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis.

Radiology
Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limitations, particularly for patients with bulbar impairment. Purpose To determine the value of deep le...

Anatomy-derived 3D Aortic Hemodynamics Using Fluid Physics-informed Deep Learning.

Radiology
Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important biomarkers for risk assessment. However, long acquisition times and cumbersome data analysis limit wide...

Deep Learning-based Quantitative CT Myocardial Perfusion Imaging and Risk Stratification of Coronary Artery Disease.

Radiology
Background Precise assessment of myocardial ischemia burden and cardiovascular risk stratification based on dynamic CT myocardial perfusion imaging (MPI) is lacking. Purpose To develop and validate a deep learning (DL) model for automated quantificat...