Jeff Hall

In a prospective study of over 600 patients, researchers found that magnetic resonance imaging (MRI) had no serious adverse effects upon the detection of tachyarrhythmias with non-MRI conditional implantable cardioverter defibrillators (ICDs).

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.

In a new survey, 83 percent of radiology residents agreed that artificial intelligence/machine learning (AI/ML) should be part of their curriculum but approximately 24 percent of residents said there are currently no AI/ML educational offerings in their residency program.

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.

For premenopausal women with regular menstrual cycles, researchers have found a significant association between the timing of menstrual cycles and the degree of background parenchymal enhancement (BPE) on contrast-enhanced mammography (CEM).

In a multicenter study involving nearly 300 patients, researchers found that abbreviated magnetic resonance imaging (MRI) had a sensitivity rate of 88.2 percent and a specificity rate of 89.1 percent for detecting early-stage hepatocellular carcinoma (HCC).

In a recent video interview, Amar Kishan, M.D., discussed a new study that demonstrated significant side effect reduction when utilizing magnetic resonance imaging (MRI) guidance instead of computed tomography (CT) guidance for stereotactic body radiation therapy (SBRT) to treat localized prostate cancer.

In a new survey that examined perceptions of breast cancer risk among more than 1,800 women who had a recent mammogram, 65 percent noted that being overweight or obese was a greater risk factor than breast density, and over a quarter of those interviewed noted they were not aware that they could reduce their breast cancer risk.

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.

Noting the significant administrative fees for the Independent Dispute Resolution (IDR) process of the No Surprises Act and onerous restrictions that have led to a nearly “non-existent” use of batching of disputed claims in radiology, the American College of Radiology (ACR) has sent formal recommendations to the United States Departments of Health and Human Services, Labor, and Treasury for addressing these issues.

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.

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.

In the second part of a recent interview, Nina Kottler, M.D., M.S., discussed keys to evaluating the potential value of artificial intelligence (AI) systems and emerging developments with AI that were discussed at the recent Radiological Society of North America (RSNA) conference.

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.

While breast cancer imaging guidelines recommend annual screening for five years after treatment for ductal carcinoma in situ (DCIS), a new study of over 12,000 women found that only 52 percent had consistent surveillance screening with researchers noting disproportionately lower follow-up imaging rates for Black and Hispanic women.

A new study found that approximately one-third of men with prostate cancer were diagnosed with at least one vertebral fracture prior to the initiation of androgen deprivation therapy (ADT).

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.

A new comparative study found the use of prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) led to treatment changes in nearly 20 percent of patients with high-risk prostate cancer.

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).

Emerging research suggests that magnetic resonance imaging (MRI) may help downgrade nearly 10 percent of suspected BI-RADS 3 lesions from ultrasound imaging

In a study looking at acute muscle injuries in professional athletes, the use of magnetic resonance imaging (MRI) within a week prior to their return to play demonstrated significant re-injury risk with intermuscular edema, callus gap and transversal and/or mixed connective tissue gap.

An inhaled contrast agent used with magnetic resonance imaging (MRI), Xenoview™ (xenon Xe 129 hyperpolarized) reportedly provides regional mapping of lung ventilation without patient exposure to ionizing radiation.

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.

Emerging research suggests that a ketogenic diet reduces the uptake of 18F-fluorodeoxyglucose (18F-FDG) in brain positron emission tomography (PET) scans with a more pronounced effect in the precuneus.

In a video interview discussing one of her recent lectures at the Radiological Society for North America (RSNA) conference, Nina Kottler, M.D., M.S., noted how the combination of artificial intelligence (AI) and radiologist experience can help mitigate bias limitations with the development of AI algorithms as well as educational biases inherent to a radiologist’s training and experience.

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