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 on Diagnostic Imaging:
Recently, NYU Langone Health partnered with Facebook AI in a collaboration that has produced MRI images that are the same diagnostic quality as standard MRI images. But, these scans are four times as fast, and the quality is virtually indistinguishable from a scan that takes much longer. In this edition of The Reading Room, published later this week, Dr. Michael Recht, chair of the NYU Langone Health radiology department, discusses the partnership, how the team has successfully reached this point, and what comes next.
For additional episodes of The Reading Room, click here.
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Last week, PLOS ONE published a study about how exposure to diagnostic imaging, such as X-rays and CT scans, can significantly raise a man’s risk of testicular cancer. In reporting the nearly 60-percent increase in risk, the researchers suggested lead shielding to reduce radiation exposure. But, the conversation around – and recommendations for – lead shielding of gonads has changed significantly. This week, Diagnostic Imaging spoke with Cynthia McCullough, Ph.D., Chair of the Board for the American Association of Physicists in Medicine, about how the guidance for lead shielding has evolved and what that means for providers in practice.
To read the PLOS ONE story, click here.
Sports are great for enjoyment and cardiovascular health, but the physical exertion can come with some wear-and-tear on the body. Later this week, look for coverage about the impact of some types of sports that can exacerbate orthopedic conditions.
To read more of Diagnostic Imaging’s coverage of sports, 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.