Predictions of heart attacks and deaths based on coronary computed tomography angiography (CCTA) are more accurate when made using an artificial intelligence (AI) algorithm than with the Coronary Artery Disease Reporting and Data System (CAD-RADS) or other risk assessment methods.

A new CT- and PET-imaging-based approach—one that entails applying big data to personalizing treatment protocols—is needed to better identify which head and neck carcinoma (HNC) patient subgroups respond to which specific therapies.

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With the “volume to value” movement pushing radiologists to prove their contributions to cost containment, some are feeling uneasy. After all, imaging utilization stands to be curbed—or at least eyed more closely than ever before for appropriateness.

Positron emission tomography/computed tomography (PET/CT) shows a strong correlation between severe obstructive sleep apnea (OSA) and impaired coronary flow, making it an important tool for helping to avert cardiac complications that occur when OSA is left untreated.

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Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday.