Radiology

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose...

An Improved Combination of Faster R-CNN and U-Net Network for Accurate Multi-Modality Whole Heart Segmentation.

Detailed information of substructures of the whole heart is usually vital in the diagnosis of cardio...

Federated Learning of Generative Image Priors for MRI Reconstruction.

Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit privac...

Feasibility of AI-assisted compressed sensing protocols in knee MR imaging: a prospective multi-reader study.

OBJECTIVES: To evaluate the image quality and diagnostic performance of AI-assisted compressed sensi...

Appropriate Reliance on Artificial Intelligence in Radiology Education.

Users of artificial intelligence (AI) can become overreliant on AI, negatively affecting the perform...

Commercially Available Artificial Intelligence Algorithms of Interest to Pediatric Radiology: The Growing Gap Between Potential Use and Data Training.

Ultrasound Localization Microscopy Using Deep Neural Network.

Noninvasive imaging of microvascular structures in deep tissues provides morphological and functiona...

Non-contrast CT synthesis using patch-based cycle-consistent generative adversarial network (Cycle-GAN) for radiomics and deep learning in the era of COVID-19.

Handcrafted and deep learning (DL) radiomics are popular techniques used to develop computed tomogra...

Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI.

The objective of this IRB approved retrospective study was to apply deep learning to identify magnet...

Deep learning-aided extraction of outer aortic surface from CT angiography scans of patients with Stanford type B aortic dissection.

BACKGROUND: Guidelines recommend that aortic dimension measurements in aortic dissection should incl...

Multi-institutional Prognostic Modeling in Head and Neck Cancer: Evaluating Impact and Generalizability of Deep Learning and Radiomics.

UNLABELLED: Artificial intelligence (AI) and machine learning (ML) are becoming critical in developi...

Fast high-quality MRI protocol of the lumbar spine with deep learning-based algorithm: an image quality and scanning time comparison with standard protocol.

OBJECTIVE: The objective of this study is to prospectively compare quantitative and subjective image...

The use of weather nowcasting convolutional neural network extrapolators in cardiac PET imaging.

INTRODUCTION: Algorithms to predict short-term changes in local weather modalities have been used in...

Accelerated diffusion-weighted imaging of the prostate using deep learning image reconstruction: A retrospective comparison with standard diffusion-weighted imaging.

PURPOSE: Routine multiparametric MRI of the prostate reduces overtreatment and increases sensitivity...

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional rad...

Deep learning and ultrasound feature fusion model predicts the malignancy of complex cystic and solid breast nodules with color Doppler images.

This study aimed to evaluate the performance of traditional-deep learning combination model based on...

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

BACKGROUND: Conventional MRI staging can be challenging in the preoperative assessment of rectal can...

Initial study on an expert system for spine diseases screening using inertial measurement unit.

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems w...

Learning curves for robotic-assisted spine surgery: an analysis of the time taken for screw insertion, robot setting, registration, and fluoroscopy.

PURPOSE: The purpose of this study was to clarify the learning curve for robotic-assisted spine surg...

What benefit can be obtained from magnetic resonance imaging diagnosis with artificial intelligence in prostate cancer compared with clinical assessments?

The present study aimed to explore the potential of artificial intelligence (AI) methodology based o...

MR-zero meets RARE MRI: Joint optimization of refocusing flip angles and neural networks to minimize T -induced blurring in spin echo sequences.

PURPOSE: An end-to-end differentiable 2D Bloch simulation is used to reduce T induced blurring in si...