Finding such discrepancies is critical to the continuity of patient care, as medical records and reports are often utilized across multiple providers and facilities.
With the earth’s warmest March on record in 175 years barely in the rearview mirror, a team of radiology experts says medical imaging must step up its sustainability efforts.
Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.
Dense breast experts Wendie Berg, MD, and JoAnn Pushkin, executive director of DenseBreast-info Inc., explain the current status of breast density patient inform laws, reimbursement and new technologies to aid cancer detection.
Recently a radiomics-based machine learning model proved highly accurate at predicting which patients would develop pancreatic cancer three to 36 months after abdominal CT imaging.
Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.
Raquel Roman, chair of the Radiology Business Management Association (RBMA) Young Professionals Committee, and director of growth at Essential Radiology, explains how the group mentors the next generation leaders.
![Advanced artificial intelligence (AI) models can evaluate cardiovascular risk in routine chest CT scans without contrast, according to new research published in Nature Communications.[1] In fact, the authors noted, the AI approach may be more effective at identifying issues than relying on guidance from radiologists. Representative non-contrast CT slices for two patients (left), with super-imposed segmentations (right). One artificial intelligence (AI) model was used to segment a cardiac mask.](/sites/default/files/styles/stack_views/public/2024-04/screen_shot_2024-04-23_at_10.44.32_am.png.webp?itok=vXt3hxDo)
