The AI software assisted in various tasks, such as segmenting, labeling and measuring normal structures, providing an automated analysis of pulmonary, cardiac and musculoskeletal findings.

Testing AI’s ability to detect carotid artery disease on ultrasound, U.K. researchers have found their algorithm achieved 90% accuracy, along with 87% sensitivity and 82% specificity, at the task.

During one surge, the hospital’s CT volumes spiked to 55% higher than pre-pandemic levels—no doubt due to heavy demand for advanced chest imaging.

COVID-19 set back screening mammography further than any other category of breast care, dropping schedule adherence during infection peaks to 36% of pre-pandemic rates.

In addition to CE-MRI's increased sensitivity for identifying breast cancers, the researchers also found the modality had superior negative likelihood ratios with higher pre-test probabilities for safely ruling out malignancy. 

The funds will be allocated specifically toward neuroimaging and cognitive and immunological tests to explore if and how mild and asymptomatic SARS-CoV-2 infections have a long-lasting impact on neurocognitive function. 

A new analysis suggests that follow-up imaging protocols for certain lung nodules identified via CT may need to be revisited. 

Combining CT lung measurements with machine learning models to predict prognosis in COPD patients could help to lessen their reliance on emergency services.