Radiology

Artificial Intelligence as an Add-On Instrument in Fetal Ultrasound; Sonographers' and Obstetricians' Expectations.

OBJECTIVE: Artificial intelligence applications (AIA) in fetal ultrasound are rapidly evolving, yet their integration into routine clinical practice remains limited. This study explores the attitudes, expectations and concerns of obstetric sonographers and gynecologists regarding AIA as a diagnostic aid during fetal anomaly scans. METHOD: An online survey was distributed to sonographers, midwives,...

Transformer-based super-resolution lung CT images improve visualization of multiple diseases.

OBJECTIVES: To assess the Transformer-based Swin2SR model for super-resolution (SR) enhancement of lung CT images and its clinical potential. METHODS: Chest CT scans from 303 patients at three hospitals were retrospectively included. Standard 512-matrix images were enhanced to 1024- and 2048-matrix versions (SR-1024, SR-2048). Image noise and signal-to-noise ratio (SNR) for lung tissue, muscle, an...

Artificial Intelligence prognostication of liver disease using imaging.

Accurate prognostic tools in patients with chronic liver disease (CLD) have the potential to improve clinical outcomes and reduce health care costs. I...

Radiomics-Based Classification of Pathological Patterns in Common Carotid Artery Wall.

OBJECTIVES: Abnormal echogenic patterns such as the triple signal pattern can be identified in the common carotid artery (CCA) using ultrasound. These...

Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.

OBJECTIVE: The primary aim of this study was to develop and internally validate ultrasound-based radiomics models to discriminate between all types of...

3d elastic-modulus imaging using ultrasound linear arrays and efficient data-driven training strategies.

We are developing ultrasonic-based techniques for elastic modulus imaging throughout a tissue volume using the autoprogressive (AutoP) method with lin...

Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?

OBJECTIVE: We evaluated a commercial artificial intelligence (AI) system as a concurrent decision-support tool for clinically significant prostate can...

Keypoint detection network for needle localization on intra-procedural MRI in MRI-guided liver interventions.

PURPOSE: Segmentation neural networks have demonstrated promising results for interventional needle localization on MRI. However, these networks requi...

Automatic Segmentation of Placenta from MR images Using a Novel BiGC U-Net.

Accurate segmentation of the placenta in Magnetic Resonance (MR) images is required for quantitative techniques such as texture and shape analysis, wh...

Viewpoint on the Consequences and Mitigation of Cognitive Bias in the Radiological Interpretation of Breast Cancer Imaging Using Artificial Intelligence.

Artificial intelligence (AI) is increasingly integrated into breast imaging workflows, offering the potential to enhance diagnostic accuracy, efficien...

Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.

INTRODUCTION: Prostate-specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone rang...

Imaging Modalities in Tuberculosis.

Tuberculosis (TB) remains a major global health challenge, with increasing prevalence of multidrug-resistant and extrapulmonary forms complicating dia...

A unified deep learning framework for cross-platform harmonization of multi-tracer PET quantification in neurodegenerative disease.

Quantitative PET underpins diagnosis and treatment monitoring in neurodegenerative disease, yet systematic biases between PET-MRI and PET-CT preclude ...

Deep Learning for Medical Ultrasound Image Segmentation: A Systematic Review of the Current Research.

Deep learning (DL) has enabled automated segmentation of ultrasound images, and due to the rapid development of DL models, we want to offer a comprehe...

Diagnostic Improvement in Endoleak Detection: The Role of Low-energy Virtual Monochromatic CT and Deep Learning Reconstruction.

To assess the diagnostic performance of low-energy virtual monochromatic CT imaging (VMI) combined with deep learning image reconstruction (DLIR) for ...

Dual-energy CT clot and peri-clot radiomics for predicting complete reperfusion and clinical outcome following endovascular therapy in acute ischemic stroke.

OBJECTIVES: Complete reperfusion is the optimal technical goal of endovascular therapy (EVT) and is closely linked to favorable outcomes in acute isch...

Image-Based Medical Navigation Systems for Cardiac Interventions: Recent Technological Advances.

BACKGROUND: The application of catheter-based treatments for a growing range of structural heart diseases (SHD) has significantly increased over the p...

Brain volumetric variability and artificial intelligence diagnosis: Importance of race/ethnicity-specific reference standards and social determinant adjustment. A scoping review.

Neuroimaging techniques such as magnetic resonance imaging (MRI) are routinely used in diagnostic radiology to evaluate brain changes associated with ...

Altered degree centrality and resting-state functional connectivity in epilepsy patients with focal to bilateral tonic-clonic seizures.

Focal to bilateral tonic-clonic seizures (FBTCS) is a severe form of seizure associated with various adverse events. This study aimed to characterize ...

Using U-Nets to Predict the Effects of Head Motion on Simulated Specific Absorption Rate for Ultra-High Field Magnetic Resonance Imaging With Parallel Transmission.

PURPOSE: Ultrahigh-field MRI requires careful management of the specific absorption rate (SAR), which is subject and subject-position dependent. Withi...