GE HealthCare announced today the 510(k) submission to the U.S. Food and Drug Administration for CleaRecon DL, deep learning technology, designed to improve the quality of cone-beam computed tomography (CBCT) images by bringing artificial intelligence (AI)-based 3D reconstruction to the interventional suite. This technology, which is pending 510(k) clearance, will be demonstrated at the Radiological Society of North America’s (RSNA) 2024 Annual Meeting taking place from December 1-4 in Chicago.
As demand for minimally invasive procedures continues to grow, GE HealthCare is committed to helping clinicians use image guidance technologies to their full potential by removing barriers with the goal of helping providers achieve better clinical and operational outcomes.
To be available on Allia Image-Guided Solutions (IGS) Systems, CleaRecon DL is designed to use AI-based reconstruction to improve image quality by removing streaks without introducing additional artifacts. It is also designed to improve CBCT analysis through clear images. This technology aims to advance the company’s vision to accelerate CBCT adoption in daily practice.
“GE HealthCare has been a leader in CBCT for more than two decades – offering continued innovation through offerings such as augmented guidance solutions designed to improve procedure outcomes, as well as developing a wide-bore platform capable of acquiring CBCT with obese patients, even with their arms down,” said Arnaud Marie, General Manager, Global Intervention at GE HealthCare. “Over the years, artifacts created in scans through natural movement of the body and the distribution of contrast have posed another significant challenge to clinicians when obtaining CBCT images – posing a major barrier to adoption of CBCT technology. We designed CleaRecon DL to address this challenge so physicians and their patients can benefit from this advanced imaging technology.”
In addition to CleaRecon DL, GE HealthCare is also introducing OnWatch Predict, predictive monitoring of interventional image-guided systems to provide enhanced service and increase system availability for on-time diagnosis, invasive procedures and treatments. Combining ongoing system diagnosis with user interface monitoring, image chain health and X-ray generation, OnWatch Predict is designed to forecast component failure, allowing clinicians to schedule service before an issue occurs with the system and limiting unplanned downtime.
