The new artificial intelligence-powered software from Nanox reportedly identifies findings suggestive of compression fractures and low density on computed tomography (CT) images and facilitates more precise measurement of these fractures.
Seventy-five percent of compression fractures are misdiagnosed or not reported, according to the World Congress of Osteoporosis. However, an emerging artificial intelligence (AI) software platform, which just received FDA 510(k) clearance, may help improve the assessment of vertebral compression fractures with computed tomography (CT).
Nanox said the AI software HealthOST provides enhanced qualitative and quantitative analysis of spine CT images to help assess patients for musculoskeletal diseases such as osteoporosis. This analysis includes:
• labeling of T1-L4 vertebrae
• measurement of height loss in each of these vertebra
• measurement of mean Hounsfield units (HUs) in volume of interest within vertebra (T11-L4)
Indicated for patients 50 years of age and older who are getting CT imaging for at least four vertebrae in either the T1-L4 or T11-L4 areas of the spine, HealthOST helps detect findings that are suspicious for possible compression fractures and low bone density, according to Nanox.
“With the FDA clearance of HealthOST, we are thrilled to offer radiologists a new tool that provides deeper analysis of medical images to support identifying these patients who may be at risk of developing prevalent musculoskeletal conditions, such as osteoporosis, to help promote further workup and treatment of these patients,” noted Pini Ben Elazar, the general manager of Nanox.AI.
Could AI-Powered Abbreviated MRI Reinvent Detection for Structural Abnormalities of the Knee?
April 24th 2025Employing deep learning image reconstruction, parallel imaging and multi-slice acceleration in a sub-five-minute 3T knee MRI, researchers noted 100 percent sensitivity and 99 percent specificity for anterior cruciate ligament (ACL) tears.
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.