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FDA Clears Emerging AI-Powered Software for Brain MRI
Leveraging AI technology, the AiMIFY software reportedly facilitates double the contrast enhancement in comparison to gadolinium-based contrast agents (GBCAs) for brain MRI.
Offering enhanced imaging capability for detection of lesions on brain magnetic resonance imaging (MRI), the artificial intelligence (AI)-enabled software AiMIFY has been granted clearance from the Food and Drug Administration (FDA) for a class II software as a medical device (SaMD).
Co-developed by Bracco Diagnostics and Subtle Medical, AiMIFY enhances contrast on brain MRI to a level that is double the contrast generated with labeled dosing of gadolinium-based contrast agents (GBCAs). The increased imaging clarity bolsters the detection of small lesions on brain MRI, according to the companies.
The recently FDA-cleared AI software AiMIFY reportedly enhances contrast on brain MRI to a level that is double the contrast generated with labeled dosing of gadolinium-based contrast agents (GBCAs). (Image courtesy of Adobe Stock.)
The companies emphasized research validation of aiMIFY use with a variety of vendor scanners and MRI sequences as well as datasets including diverse patient demographics and pathologies.
"By integrating Bracco's expertise in contrast imaging with Subtle Medical's cutting-edge deep-learning technology, we are poised to redefine diagnostic precision and efficiency, setting new standards in the field for the ultimate benefit of the patients,” noted Fulvio Renoldi Bracco, the vice chairman and CEO of Bracco Imaging.
Seven Takeaways from New CT and MRI Guidelines for Ovarian Cancer Staging
January 20th 2025In an update of previous guidelines from the European Society of Urogenital Radiology published in 2010, a 21-expert panel offered consensus recommendations on the utility of CT, MRI and PET-CT in the staging and follow-up imaging for patients with ovarian cancer.
Can MRI-Based AI Enhance Risk Stratification in Prostate Cancer?
January 13th 2025Employing baseline MRI and clinical data, an emerging deep learning model was 32 percent more likely to predict the progression of low-risk prostate cancer (PCa) to clinically significant prostate cancer (csPCa), according to new research.
