Radiology

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...

A predictive signal model for dynamic cardiac magnetic resonance imaging.

Robust dynamic cardiac magnetic resonance imaging (MRI) has been a long-standing endeavor-as real-ti...

Simulator, machine learning, and artificial intelligence: Time has come to assist prenatal ultrasound diagnosis.

In this Commentary authors investigated and extended the role of simulator in assisting obstetric so...

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set.

OBJECTIVE: Characterization of the optic nerve through measurement of optic nerve diameter (OND) and...

A unified hybrid transformer for joint MRI sequences super-resolution and missing data imputation.

High-resolution multi-modal magnetic resonance imaging (MRI) is crucial in clinical practice for acc...

Accelerated Measurement of Carotid Plaque Volume Using Artificial Intelligence Enhanced 3D Ultrasound.

BACKGROUND: Carotid plaque volume (CPV) can be measured by 3D ultrasound and may be a better predict...

Fetal magnetic resonance imaging artifacts: role of deep learning to improve imaging.

Radiology as a Specialty in the Era of Artificial Intelligence: A Systematic Review and Meta-analysis on Medical Students, Radiology Trainees, and Radiologists.

RATIONALE AND OBJECTIVES: Artificial intelligence (AI) is changing radiology by automating tasks and...

Fully automated segmentation and radiomics feature extraction of hypopharyngeal cancer on MRI using deep learning.

OBJECTIVES: To use convolutional neural network for fully automated segmentation and radiomics featu...

Computerised emergency work-up for mass-like brain MRI lesions: can explainable AI support radiologists?

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investi...