FDA Clears Mobile C-Arm Device that May Accelerate Fluoroscopic and 3D CT Imaging
Offering ease of mobility and self-driving capabilities, the Ciartic Move C-arm device reportedly reduces the stress and potential for error associated with manual repositioning during intraoperative imaging with computed tomography and fluoroscopy.
The Food and Drug Administration (FDA) has granted 510(k) clearance for the Ciartic Move C-arm device, which facilitate enhanced efficiency with intraoperative fluoroscopic and 3D cone-beam computed tomography (CT) imaging in the OR.
Providing full motorization from the C-arm down to the wheels of the device, the Ciartic Move has self-driving capabilities that may enable better consistency with intraoperative imaging and more automated imaging workflows, according to Siemens Healthineers, the manufacturer of the Ciartic Move.
In comparison to manual adjustments with conventional C-arm devices, the recently FDA-cleared Ciartic Move device reportedly offers self-driving capabilities and storage of up to 12 procedure-specific positions with associated imaging parameters, according to Siemens Healthineers, the manufacturer of the device. (Image courtesy of Siemens Healthineers.)
Noting the device’s ability to reduce the time and workforce capacity associated with manual adjustments of conventional C-arm devices, Siemens Healthineers pointed out that the Ciartic Move device allows clinicians to store up to 12 procedure-specific positions with associated imaging parameters.
“With the FDA clearance of the Ciartic Move, Siemens Healthineers proudly introduces our first self-driving mobile C-arm, which can provide much-needed relief for overtaxed operating room teams by automating and accelerating intraoperative imaging workflows to a previously unseen degree,” said April Grandominico, vice president for surgical therapies in the Advanced Therapies business at Siemens Healthineers North America.
What is the Best Use of AI in CT Lung Cancer Screening?
April 18th 2025In comparison to radiologist assessment, the use of AI to pre-screen patients with low-dose CT lung cancer screening provided a 12 percent reduction in mean interpretation time with a slight increase in specificity and a slight decrease in the recall rate, according to new research.
The Reading Room: Racial and Ethnic Minorities, Cancer Screenings, and COVID-19
November 3rd 2020In this podcast episode, Dr. Shalom Kalnicki, from Montefiore and Albert Einstein College of Medicine, discusses the disparities minority patients face with cancer screenings and what can be done to increase access during the pandemic.
Can CT-Based AI Radiomics Enhance Prediction of Recurrence-Free Survival for Non-Metastatic ccRCC?
April 14th 2025In comparison to a model based on clinicopathological risk factors, a CT radiomics-based machine learning model offered greater than a 10 percent higher AUC for predicting five-year recurrence-free survival in patients with non-metastatic clear cell renal cell carcinoma (ccRCC).
Could Lymph Node Distribution Patterns on CT Improve Staging for Colon Cancer?
April 11th 2025For patients with microsatellite instability-high colon cancer, distribution-based clinical lymph node staging (dCN) with computed tomography (CT) offered nearly double the accuracy rate of clinical lymph node staging in a recent study.
