Radiology

Patient Perspectives on Artificial Intelligence in Radiology.

There are two major areas for patient engagement in radiology artificial intelligence (AI). One is i...

A Patient's Wish List for Imaging Artificial Intelligence (and Radiology).

Artificial Intelligence in Radiology: A Fast-Food Versus Slow-Food Question?

Machine Learning Classification of Body Part, Imaging Axis, and Intravenous Contrast Enhancement on CT Imaging.

The development and evaluation of machine learning models that automatically identify the body part...

Development of a novel combined nomogram integrating deep-learning-assisted CT texture and clinical-radiological features to predict the invasiveness of clinical stage IA part-solid lung adenocarcinoma: a multicentre study.

AIM: To develop a novel combined nomogram based on deep-learning-assisted computed tomography (CT) t...

Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.

Since the beginning of the COVID-19 pandemic, new and non-invasive digital technologies such as arti...

Clinical Applications, Challenges, and Recommendations for Artificial Intelligence in Musculoskeletal and Soft-Tissue Ultrasound: Expert Panel Narrative Review.

Artificial intelligence (AI) is increasingly used in clinical practice for musculoskeletal imaging t...

Diagnostic radiology and its future: what do clinicians need and think?

OBJECTIVE: To investigate the view of clinicians on diagnostic radiology and its future.

Multi-institutional PET/CT image segmentation using federated deep transformer learning.

BACKGROUND AND OBJECTIVE: Generalizable and trustworthy deep learning models for PET/CT image segmen...

PhysVENeT: a physiologically-informed deep learning-based framework for the synthesis of 3D hyperpolarized gas MRI ventilation.

Functional lung imaging modalities such as hyperpolarized gas MRI ventilation enable visualization a...

Improving image quality with super-resolution deep-learning-based reconstruction in coronary CT angiography.

OBJECTIVES: To evaluate the effect of super-resolution deep-learning-based reconstruction (SR-DLR) o...

Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction.

Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion i...

Noise2Recon: Enabling SNR-robust MRI reconstruction with semi-supervised and self-supervised learning.

PURPOSE: To develop a method for building MRI reconstruction neural networks robust to changes in si...

Lumbar Spinal Canal Segmentation in Cases with Lumbar Stenosis Using Deep-U-Net Ensembles.

BACKGROUND: Narrowing of the lumbar spinal canal, or lumbar stenosis (LS), may cause debilitating ra...

Use of artificial intelligence and deep learning in fetal ultrasound imaging.

Deep learning is considered the leading artificial intelligence tool in image analysis in general. D...

Novel deep learning method for coronary artery tortuosity detection through coronary angiography.

Coronary artery tortuosity is usually an undetected condition in patients undergoing coronary angiog...

Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans.

PURPOSE: The aim of this study was to implement and evaluate a quality assurance (QA) workflow that ...

AI tools in Emergency Radiology reading room: a new era of Radiology.

Artificial intelligence tools in radiology practices have surged, with modules developed to target s...

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer.

BACKGROUND: To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep le...

Preoperative Prediction of Microvascular Invasion with Gadoxetic Acid-Enhanced Magnetic Resonance Imaging in Patients with Single Hepatocellular Carcinoma: The Implication of Surgical Decision on the Extent of Liver Resection.

INTRODUCTION: Microvascular invasion (MVI) is one of the most important prognostic factors for hepat...

Harnessing the Potential of Artificial Intelligence for Quality Assurance in Radiology Practice.