In a recent video interview, Sonia Gupta, MD discussed a number of ongoing developments with artificial intelligence (AI) in radiology, ranging from market consolidation of AI vendors to maximizing automation and efficiency with patient triage, reporting and follow-up of incidental findings.
Researchers showed that adjunctive use of a deep learning algorithm resulted in an eight percent increase in sensitivity and a nearly 10 percent increase in specificity for differentiating between colon carcinoma and acute diverticulitis on computed tomography (CT) scans.
Catch up on the top radiology content of the past week.
Preliminary research suggests the use of photon-counting detector computed tomography (CT) may facilitate a 25 percent reduction of iodinated contrast media (ICM) in comparison to energy-integrating detector CT for angiographic imaging of the thoracoabdominal aorta.
Employing deep learning capabilities, the DeepVessel FFR reportedly provides enhanced non-invasive evaluation of coronary arteries through semi-automated analysis of coronary computed tomography angiography (CCTA) imaging.
Catch up on the top AI-related news and research of the past month.
Trained and developed on over 35,000 low-dose computed tomography (LDCT) scans and validated in three independent data sets, a deep learning algorithm demonstrated an average area under the curve (AUC) of 90.6 percent for predicting lung cancer within one year.
Catch up on the top radiology content of the past week.
In a provocative new article, radiology researchers discuss the impact of social determinants of health (SDoH) upon access to care and patient outcomes, and present strategies within the realms of radiology education, research, clinical care, and innovation that may help mitigate health-care disparities.
Review the case study and test your knowledge to make the correct diagnosis.
The artificial intelligence (AI)-enabled Viz™ Vascular Suite reportedly allows automated detection of vascular conditions, shown on computed tomography (CT) and other imaging modalities, and facilitates timely triage among interdisciplinary teams.
Catch up on the top radiology content of the past week.
In comparison to primary lung cancer, pulmonary metastases had a 33 percent higher frequency of ring-like peripheral high iodine concentration on dual-energy computed tomography (DECT), according to a new retrospective study.
Review the case study and test your knowledge to make the correct diagnosis.
From incidental findings and screening for chronic obstructive pulmonary disease (COPD) to surveillance imaging protocols and the advent of artificial intelligence (AI), the authors of a new meta-analysis examine insights and emerging trends from the last two decades of research on the use of low-dose computed tomography (CT) in lung cancer screening.
Review the case study and test your knowledge to make the correct diagnosis.
Is there an optimal pace for navigating the ebbs and flows of our worklists in radiology?
Catch up on the most well-viewed video interviews from Diagnostic Imaging in 2022.
Touching on a variety of topics in radiology, here are the top five most well-read articles from Diagnostic Imaging in 2022.
Check out the top radiology content of the past week.
Catch up on the most well-read computed tomography (CT) articles from 2022.
In a recently published prospective study comparing photon-counting detector computed tomography (PCD CT) versus energy-integrating detector CT (EID CT) in patients with persistent symptoms after COVID-19 infection, researchers found that PCD CT discovered additional lung abnormalities in half of the study participants.
Catch up on the top AI-related news and research of the past month.
Check out the top radiology content of the past week.
Employing an artificial intelligence (AI) tool to quantify airway-to-artery (AAR) diameter ratios on chest computed tomography (CT), researchers found the percentage of airways with an AAR greater than 1 was associated with increased pulmonary exacerbations in ever-smokers.
In a new study based on five- to six-year follow-up data from over 650,000 children and young adults who had at least one computed tomography (CT) exam prior to the age of 22, researchers found a “strong dose-response relationship” between increased CT radiation exposure and brain cancer.
