Code is for vertebral compression fracture detection with CT scans.
Zebra Medical Vision announced July 7 it has received approval of its CPT application for using artificial intelligence (AI) with CT scans to detect vertebral compression fractures (VCF) from the American Medical Association (AMA).
This is the first AI CPT code specific to radiology.
“The latest development regarding the CPT code approval by the AMA is an industry milestone in the effort to boost the adoption of AI in imaging for VCFs and other under-diagnosed chronic conditions for which can help reveal and drive care,” said Zohar Elhanani, Zebra Medical Vision chief executive officer. “Radiologists will now be able to identify more patients with undiagnosed fractures and provide better care for patients who may be vulnerable.”
Osteoporotic fractures affect nearly 50 percent of men and 25 percent of women during their lifetimes, resulting in 2 million broken bones annually and an estimated $52 billion cost to the U.S. healthcare system. Still, Zebra Medical officials said, 75 percent of these fractures go undetected.
According to existing data, Zebra Medical’s VCF solution can increase detection rates, bringing needed treatment to more patients without addition imaging or radiation. With this CPT code in place, providers using the tool will be able to submit for reimbursement, potentially increasing its use.
For more coverage based on industry expert insights and research, subscribe to the Diagnostic Imaging e-Newsletter here.
Study Assesses Potential of Seven-Minute AI-Enhanced 3T MRI of the Shoulder
February 20th 2025Researchers found that the use of seven-minute threefold parallel imaging-accelerated deep learning 3T MRI had 89 percent sensitivity for supraspinatus-infraspinatus tendon tears and 93 percent sensitivity for superior labral tears.
Can CT-Based AI Provide Automated Detection of Colorectal Cancer?
February 14th 2025For the assessment of contrast-enhanced abdominopelvic CT exams, an artificial intelligence model demonstrated equivalent or better sensitivity than radiologist readers, and greater than 90 percent specificity for the diagnosis of colorectal cancer.