Radiology

Applying artificial intelligence to the use of ultrasound as an educational tool: A focus on ultrasound-guided regional anesthesia.

Application of synthetic data in the training of artificial intelligence for automated quality assurance in magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging scanner faults can be missed during routine quality assurance (QA) if they are subtle, intermittent, or the test being performed is insensitive to the type of fault. Coil element malfunction is a common fault within MRI scanners, which may go undetected for quite some time. Consequently, this may lead to poor image quality and the potential for misdiagnoses.

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (...

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent...

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging.

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligen...

From basic sciences and engineering to epileptology: A translational approach.

Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the r...

Parallelized ultrasound homodyned-K imaging based on a generalized artificial neural network estimator.

The homodyned-K (HK) distribution model is a generalized backscatter envelope statistical model for ultrasound tissue characterization, whose paramete...

Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in...

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.

Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis rem...

Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.

OBJECTIVE: To investigate the image quality and lesion conspicuity of a deep-learning-based contrast-boosting (DL-CB) algorithm on double-low-dose (DL...

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margi...

Deep Learning Prediction for Distal Aortic Remodeling After Thoracic Endovascular Aortic Repair in Stanford Type B Aortic Dissection.

PURPOSE: This study aimed to develop a deep learning model for predicting distal aortic remodeling after proximal thoracic endovascular aortic repair ...

Thin-slice Two-dimensional T2-weighted Imaging with Deep Learning-based Reconstruction: Improved Lesion Detection in the Brain of Patients with Multiple Sclerosis.

PURPOSE: Brain MRI with high spatial resolution allows for a more detailed delineation of multiple sclerosis (MS) lesions. The recently developed deep...

Deep Learning Model for Coronary Angiography.

The visual inspection of coronary artery stenosis is known to be significantly affected by variation, due to the presence of other tissues, camera mov...

What Does DALL-E 2 Know About Radiology?

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for image generation, augmentation, and manipulation for artifici...

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.

BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomo...

AI co-pilot: content-based image retrieval for the reading of rare diseases in chest CT.

The aim of the study was to evaluate the impact of the newly developed Similar patient search (SPS) Web Service, which supports reading complex lung d...

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy ...

Equivalent radiation exposure with robotic total hip replacement using a novel, fluoroscopic-guided (CT-free) system: case-control study versus manual technique.

Accurate and precise positioning of the acetabular cup remains a prevalent challenge in total hip arthroplasty (THA). Robotic assistance for THA has i...

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

PURPOSE: There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) to...