Velacur
Description
Velacur is a portable device intended to measure the stiffness and attenuation of the liver in a non-invasive way, via measurement of liver tissue shear modulus and ultrasound attenuation. This is done by measuring the wavelength or wave speed of mechanically created shear aves within the organ of the patient. Attenuation is measured directly via the loss in power of the ultrasound beam. The device is designed to be used at the point of care, in clinics and hospitals. The device is used by a medical professional, an employee of the clinic/hospital. The activation unit is placed under the patient, while lying supine on an exam bed. The activation unit vibrates at frequencies of 40, 50, and 60 Hz, causing shear waves within the liver of the patient. The ultrasound transducer is placed on the patient’s skin, over the intercostal space, and is used to take volumetric scans of the liver while shear waves are occurring. The device includes two algorithms designed to help users detect good quality shear waves and identify liver tissue. From the scan data, the device calculates tissue stiffness and attenuation. Minor hardware and software changes were made to the device. The organ guide (cleared in K223287) was also extended to add more optional overlays on top of the liver overlay to help with optimizing the scan and training users to obtain adequate images. The significant change is the addition of a new output measure for Velacur, an ultrasound derived fat fraction (VDFF).
Detailed example
The Velacur Determined Fat Fraction combines ultrasound attenuation and backscatter coefficient measurements. The device is indicated to determine liver tissue stiffness, attenuation, and Velacur Determined Fat Fraction in a non-invasive way. VDFF is not intended to be used in pediatric patients. This device's outputs are meant to be used in conjunction with other clinical indicators in order to aid in clinical management of patients with liver diseases, including hepatic steatosis. The device is intended to be used in a clinical setting and by trained medical professionals.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
Expected benefit
Support liver stiffness measurements.
Controls / human review
ATO: Not reported; PIA: Not published