AI

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.

Emerging research suggests combined artificial intelligence (AI) assessment of digital mammography and automated 3D breast ultrasound provides enhanced detection of breast cancer in women with dense breasts and may be a viable alternative in areas where radiologists are scarce.

In a study involving patients who presented to emergency departments with acute chest pain, a deep learning model demonstrated significantly improved prediction of aortic dissection and all-cause mortality and indicated that additional pulmonary and cardiovascular testing could be deferred in seven times as many patients as suggested by conventional risk factors and testing measures.

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.

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.

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CAAS Qardia 2.0, an updated version of the CAAS Qardia echocardiography software platform, reportedly incorporates artificial intelligence (AI)-enabled workflows, and provides enhanced imaging and analysis of key cardiac measures.

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.

Check out the top radiology content of the past week.

In a new prospective study, an emerging deep learning model, which incorporates parametric mapping with quantitative ultrasound to estimate liver fat fraction, demonstrated a 90 percent sensitivity rate and a 91 percent specificity rate for diagnosing hepatic steatosis in patients with non-alcoholic fatty liver disease (NAFLD).

Catch up on the most well-read magnetic resonance imaging (MRI) articles from 2022.

Catch up on the most well-read mammography articles from 2022.

Catch up on the most well-read artificial intelligence (AI) articles from 2022.

Check out the top radiology content of the past week.

Catch up on the top AI-related news and research of the past month.

The artificial intelligence (AI) software Saige-Density™ reportedly provides automated assessment of breast density during mammography exams.

Check out the top radiology content of the past week.

Catch up on the top radiology content of the past week.

The Food and Drug Administration has granted 510(k) clearance to iQuest (Avenda Health), an artificial intelligence (AI) platform that combines findings from magnetic resonance imaging (MRI), pathology reports and biopsy results to facilitate three-dimensional mapping of prostate cancer.

The EchoGo® Heart Failure device reportedly has an 87.8 percent sensitivity rate and an 83 percent specificity rate for diagnosing heart failure with preserved ejection fraction, an increasingly prevalent but frequently misdiagnosed condition.

One study showed use of the Aorta-CAD cloud-based software led to a 62 percent reduction of missed aortic calcification that suggested aortic atherosclerosis.

Catch up on the top radiology content of the past week.

An emerging artificial intelligence algorithm, developed to estimate volumetric breast density from 3D-reconstructed digital breast tomosynthesis images, could potentially facilitate individual risk assessments for breast cancer.

Recently launched at the Radiological Society of North America (RSNA) conference, the SIGNA Experience reportedly features synergistic technologies and artificial intelligence (AI) advances that help improve the efficiency and quality of magnetic resonance imaging.

Based on a single existing chest X-ray image, the deep learning model predicts future major adverse cardiovascular events with similar performance to an established risk scoring system and may help identify people for preventive use of statin medication.

New research suggests the Viz Aortic Dissection Algorithm has a 94.2 percent sensitivity rate for detecting aortic dissection on computed tomography (CT) angiography.

Catch up on the top radiology content of the past week.

In a recently published article, researchers from Yale University discuss the pros and cons of current FDA regulations as they apply to the clearance and use of adjunctive artificial intelligence (AI) software with conventional breast cancer screening modalities such as mammography.

Emerging research revealed that a deep learning model had a nearly twofold increase in successful segmentation and reconstruction of coronary total occlusions (CTOs) on coronary computed tomography angiogram (CCTA) and a 73 percent reduction in post-processing and measurement time in comparison to a conventional manual approach.