Japan-based researchers believe the algorithm can illuminate "hidden" information contained in imaging exams, and help radiologists in their clinical decision-making.

Using CT angiography to screen for large vessel occlusion, followed immediately by thrombectomy, proved to be the cheapest option and yielded the most quality-adjusted life years. 

This most recent approval marks the fourth of its kind for Tel Aviv, Israel-based Aidoc.

The American Society of Echocardiography released the new recommendations to replace its 2007 edition.

By combining AI with coronary artery calcium scoring and other cardiac measurements, the team would have prevented 73 unnecessary scans.

The 47-page document, published Jan. 6 in the Journal of the American College of Cardiology, touches on multiple cardiac imaging modalities, rating them based on their appropriateness for examining adults and children with previously diagnosed heart defects.

Researchers may improve heart attack outcomes by zeroing in on the cellular activity that causes long-lasting damage to the heart.

Machine learning is more accurate at predicting the long-term risk of potentially life-threatening cardiac events compared to standard clinical assessments, and eventually may revolutionize cardiovascular care.

The new approach offers more comprehensive information with no loss of image quality, and may alter the care landscape for patients with coronary artery disease.

More individuals are receiving cardiac implanted devices than ever before, and as a result, the number of complications stemming from the wire connecting the device to the heart—known as a lead—are also increasing.

Patients who practiced transcendental meditation and cardiac rehabilitation increased their myocardial blood flow by 20.7%.

Not only is MRI-guided intravenous thrombolysis cost-effective, but the approach offers long-term clinical benefits for stroke patients with no known time of onset.