Upon training a machine learning model to analyze ultrasound images of the neck, researchers tested their algorithm and have found it correctly flagged likely cancerous nodules of the thyroid gland at a 97% clip.

The study yielded a malignancy rate of 8.4% for the women who presented for routine pelvic ultrasound without prior suspicion for adnexal lesions.

“High-quality artificial intelligence-generated images preserve vital information from raw PET images without the additional radiation exposure from CT scans,” experts involved in the study explained.

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.