Top Image IQs of 2021: Can You Make the Diagnosis?

In a multicenter study examining four commercially available artificial intelligence (AI) software products for chest X-rays in over 2,000 patients, researchers found sensitivity rates ranging between 33 to 61 percent for vague airspace disease and 9 to 94 percent for small pneumothorax and pleural effusion.

In the third episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss the challenges of expanded breast cancer screening amid a backdrop of radiologist shortages and ever-increasing volume on radiology worklists.

For patients discharged from the hospital with confirmed COVID-19, median five-month follow-up MRI findings revealed a greater likelihood of regional brain volume reduction, parenchymal lung abnormalities and loss of corticomedullary differentiation in the kidney in comparison to people who have not had COVID-19.

In the second episode of a three-part podcast, Anand Narayan, M.D., Ph.D., and Amy Patel, M.D., discuss recent studies published by the Journal of the American Medical Association (JAMA) that suggested moving to more of a risk-adapted model for mammography screening.

The Pulmonary Embolism (PE) Triage and Notification (PETN) module with RapidAI’s RapidPE platform reportedly demonstrated over 90 percent sensitivity and specificity rates for detection of central PE on computed tomography (CT), according to multicenter study findings presented at the 9th Annual Pulmonary Embolism Symposium.

New research suggests the capability of a mammography-based deep learning model to identify women at high risk of breast cancer led to more than triple the cancer detection rate on breast MRI in comparison to traditional risk assessment tools.