Diagnostic Imaging's A Look Ahead: May 17, 2021, to May 21, 2021
Here's what to expect this week on Diagnostic Imaging.
In this week’s preview, here are some highlights of what you can expect to see coming soon:
X-ray imaging is the oldest and one of the most common and most affordable forms of diagnostic imaging. Even though it's been around for more than 100 years and hasn’t changed a great deal in that time, there is still room for some innovation. Later this week, look for a podcast with Amol Karnick, chief executive officer of KA Imaging, and Karim S. Karim, M.D., chief technical officer at KA Imaging, about current trends with this modality and where innovations and future work can lead.
For additional articles on KA Imaging, click here.
With vaccination rates on the rise, the United States is, hopefully, seeing a turning point in the COVID-19 pandemic. But, there’s no question that healthcare, including radiology, will be forever changed by this overwhelming experience that put a crushing burden on both providers and resources. After more than a year, Singapore Hospital has compiled a list of key lessons learned that can help inform hospitals and healthcare facilities as the industry ventures to continuing providing radiology services in the safest, most effective way possible. Keep an eye open this week for their assessment.
For additional articles on Singapore Hospital and the COVID-19 pandemic, click here.
For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.
Low-dose CT screening for lung cancer has been proven to be effective in detecting cancers earlier, allowing for faster treatment that can, ultimately, save lives. Later this week, look for an article on work being done to further improve its efficacy.
For additional thought leadership on low-dose CT and lung cancer screening, click here.
Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?
June 19th 2025Emerging research shows that a multiple time-series deep learning model assessment of CT images provides 20 percent higher sensitivity than a delta radiomic model and 56 percent higher sensitivity than a clinical model for prognostic evaluation of ground-glass nodules.
