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Lung cancer screening stethoscope

“Lung cancer screening shouldn’t just be looking for nodules. That’s a small part of what we see on the CT scan.” 

Cardiologists have developed an open-source artificial intelligence (AI) model capable of accurately evaluating a long list of measurements on echocardiography results. The group detailed the development and validation of that model in the Journal of the American College of Cardiology. A comparison of measurements made by a sonographer (in red) and predictions from the deep learning model (in light blue) across 9 echocardiographic parameters. Image and caption courtesy of Ouyang et al., JACC.

The algorithm, trained on more than 150,000 TTE studies, can calculate 18 different measurements regularly used in echocardiography

Biparametric prostate MRIs take significantly less time than multiparametric exams, as they do not include a contrast-enhanced sequence. This also makes the abbreviated exams more cost-friendly. 

POCUS hand held ultrasound SonoSite iViz.

New data align with a rise in intrigue around bedside point-of-care ultrasound to address growing patient demand. 

24/7 healthcare around the clock on-call after-hours

Even before the onset of COVID-19, data suggested that rads’ after-hours workloads had doubled in proportion to the increase in emergency department visits in the years leading up to 2020.

stroke brain dementia alzheimer's puzzle mental health

Using an advanced MRI technique, experts were able to measure tissue susceptibility and detect subtle variances in iron levels throughout different regions of the brain associated with memory.

Around the web

RadNet Chaiman and CEO Howard Berger, MD, explains why the company has invested tens of millions into DeepHealth to rapidly build up a new business model. 

 

Thanks to AI, clinicians can use mammograms to do a lot more than identify signs of breast cancer. Researchers explored data from nearly 50,000 patients, presenting their findings in Heart.

A new analysis is prompting questions regarding how rigorously many of the AI-enabled tools approved by the U.S. Food and Drug Administration are evaluated prior to their clearance.