Diagnostic Radiology

Generative Adversarial Networks: A Primer for Radiologists.

Artificial intelligence techniques involving the use of artificial neural networks-that is, deep lea...

Interventional Radiology ex-machina: impact of Artificial Intelligence on practice.

Artificial intelligence (AI) is a branch of Informatics that uses algorithms to tirelessly process d...

Clinical applications of AI in MSK imaging: a liability perspective.

Artificial intelligence (AI) applications have been gaining traction across the radiology space, pro...

Assessing Rectal Cancer Treatment Response Using Coregistered Endorectal Photoacoustic and US Imaging Paired with Deep Learning.

Background Conventional radiologic modalities perform poorly in the radiated rectum and are often un...

Applications of artificial intelligence in cardiovascular imaging.

Research into artificial intelligence (AI) has made tremendous progress over the past decade. In par...

To buy or not to buy-evaluating commercial AI solutions in radiology (the ECLAIR guidelines).

Artificial intelligence (AI) has made impressive progress over the past few years, including many ap...

Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools.

The vast amount of data produced by today's medical imaging systems has led medical professionals to...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days i...

The integration of artificial intelligence in medical imaging practice: Perspectives of African radiographers.

INTRODUCTION: The current technological developments in medical imaging are centred largely on the i...

Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study.

INTRODUCTION: The integration of artificial intelligence (AI) systems into medical imaging is advanc...

Will we successfully avoid the garbage in garbage out problem in imaging data mining? An overview on current concepts and future directions in molecular imaging.

COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network.

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since Dece...

Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Artificial intelligence (AI) has been applied with considerable success in the fields of radiology, ...

Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation.

A common shortfall of supervised deep learning for medical imaging is the lack of labeled data, whic...

Diagnostic accuracy of stress-only myocardial perfusion SPECT improved by deep learning.

PURPOSE: Deep convolutional neural networks (CNN) for single photon emission computed tomography (SP...

Involvement of Machine Learning Tools in Healthcare Decision Making.

In the present day, there are many diseases which need to be identified at their early stages to sta...

RadLex Normalization in Radiology Reports.

Radiology reports have been widely used for extraction of various clinically significant information...

Deep learning in breast radiology: current progress and future directions.

This review provides an overview of current applications of deep learning methods within breast radi...

Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury.

Combat-related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cogniti...

Five principles for the intelligent use of AI in medical imaging.

Osteoarthritis year in review 2020: imaging.

This narrative "Year in Review" highlights a selection of articles published between January 2019 an...