Here's what to expect this week on Diagnostic Imaging.
Welcome to a New Year at Diagnostic Imaging! In this week’s preview, here are some highlights of what you can expect to see coming soon:
With 2020 in the rear-view mirror, there is a great deal on the horizon for radiology. Editorial Board member Mina Makary, M.D., an interventional radiologist at Ohio State University Wexner Medical Center, shares his thoughts this week about what you can expect in the coming months. Keep an eye open for his insights.
In the meantime, take another look at 2020 end-of-year coverage.
For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.
Post-traumatic stress disorder (PTSD) can be the result of several factors – both physical and psychological – and it has been the focus of several research efforts in recent years. Still, little is understood about symptoms of this condition. In a new study, investigators from the University of California at San Diego have determined that brain volume measurement has the potential to be an early biomarker. Look for details on their findings soon.
For additional PTSD and traumatic brain injury coverage, click here.
As in year’s past, artificial intelligence (AI) continues its march toward being a much more integrated part of both research and clinical activities. This week, Frost & Sullivan analysts Suresh Kuppuswamy and Siddharth Shah offer perspectives about what vendors have done to further develop AI and enterprise imaging. Look for their insights about why AI and enterprise imaging "won" RSNA 2020.
For additional enterprise imaging coverage, click here.
Comparative AI Study Shows Merits of RapidAI LVO Software in Stroke Detection
February 6th 2025The Rapid LVO AI software detected 33 percent more cases of large vessel occlusion (LVO) on computed tomography angiography (CTA) than Viz LVO AI software, according to a new comparative study presented at the International Stroke Conference (ISC).
Computed Tomography Study Assesses Model for Predicting Recurrence of Non-Small Cell Lung Cancer
January 31st 2025A predictive model for non-small cell lung cancer (NSCLC) recurrence, based on clinical parameters and CT findings, demonstrated an 85.2 percent AUC and 83.3 percent sensitivity rate, according to external validation testing in a new study.
Can MRI-Based Deep Learning Improve Risk Stratification in PI-RADS 3 Cases?
January 30th 2025In external validation testing, a deep learning model demonstrated an average AUC of 87.6 percent for detecting clinically significant prostate cancer (csPCA) on prostate MRI for patients with PI-RADS 3 assessments.