Turning to the modality just a few days earlier appeared to make a significant impact when it came to making an accurate diagnosis. 

The American Society of Nuclear Cardiology recently met with the Louisville, Kentucky, insurer, which shared an openness to reviewing a recent coverage determination. 

An international group of researchers developed a deep learning model capable of identifying accessory pathways in patients with Wolff-Parkinson-White syndrome.

Tempus, a Chicago-based healthcare technology company, collaborated with Geisinger on the solution.

Researchers tracked data from more than 1,000 patients who received care from 2014 to 2020. 

The new study, published in JAMA Cardiology, included more than 800 hypertension patients aged 75 years or older. 

The team's deep neural network was trained on more than 1.6 million 12-lead ECGs. 

The guidance, published in full in the Journal of Cardiovascular Computed Tomography, includes updates to previous CCTA recommendations and was designed to answer common questions.