FDA Clears AI-Powered Brain Volumetry Software for MRI
The artificial intelligence (AI)-enabled AIRAscore can reportedly provide quantitative brain volume data within five minutes of assessing brain MRI scans.
The Food and Drug Administration (FDA) has granted 510(k) clearance for AIRAscore, an artificial intelligence (AI)-powered software that may facilitate earlier detection of Alzheimer’s disease and other forms of dementia through quantitative brain volume analysis based on magnetic resonance imaging (MRI) scans.
AIRAMed, the developer of the AIRAscore, said the software provides detailed assessment of lobes and limbic structures in the brain to help detect patterns of neurodegenerative disease and brain volumes that are not age appropriate. The company added that other benefits of the software include auto-segmentation of white matter, cerebrospinal fluid, grey matter and T1 hypointensities.
The AIRAscore findings can be beneficial in detecting and differentiating between Alzheimer’s disease, frontotemporal dementia, and other diseases with patterns of brain volume loss, according to AIRAMed.
“For so long, we’ve been limited to reading a patient’s MRI to detect Alzheimer’s and other dementias. However, we know from several studies that patients with these brain diseases suffer from subtle brain volume loss early in their disease course that cannot be observed with the human eye,” noted Tobias Lindig, M.D., the founder and managing director of AIRAMed. “With AIRAscore, we are now offering physicians a highly precise, quantitative tool for the rapid detection of areas with a brain volume below the normal range.”
Meta-Analysis Shows Merits of AI with CTA Detection of Coronary Artery Stenosis and Calcified Plaque
April 16th 2025Artificial intelligence demonstrated higher AUC, sensitivity, and specificity than radiologists for detecting coronary artery stenosis > 50 percent on computed tomography angiography (CTA), according to a new 17-study meta-analysis.
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).
