Rayvolve

AZmed

AZmed has developed Rayvolve, an AI software that detects bone lesions on standard radiographs. Rayvolve is a SaaS product and is integrated into the radiologist's workflow.

Product specifications

Information source: Vendor
Last updated: Aug. 19, 2023

General
Product name	Rayvolve
Company	AZmed
Subspeciality	MSK
Modality	X-ray
Disease targeted	Bone fractures
Key-features	Fracture detection
Suggested use	During: perception aid (prompting all abnormalities/results/heatmaps)
Data characteristics
Population	All trauma X-rays
Input	2D X-ray
Input format	DICOM
Output	images with the regions of interest for the pathology, coordinates of the regions of interest for the pathology, risk score
Output format	DICOM
Technology
Integration	Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application
Deployment	Locally on dedicated hardware, Cloud-based
Trigger for analysis	Automatically, right after the image acquisition
Processing time	< 3 sec
Certification
CE	Certified, Class IIa , MDR
FDA	510(k) cleared, Class II
Market presence
On market since	06-2019
Distribution channels	Blackford, Wellbeing Software, GE Edison marketplace, deepcOS
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model	Subscription
Based on	Number of analyses
Evidence
Peer reviewed papers on performance	Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms (read) Comparison of diagnostic performance of a deep learning algorithm, emergency physicians, junior radiologists and senior radiologists in the detection of appendicular fractures in children (read) How Can a Deep Learning Algorithm Improve Fracture Detection on X-rays in the Emergency Room? (read) External validation of a commercially available deep learning algorithm for fracture detection in children: Fracture detection with a deep learning algorithm (read)
Non-peer reviewed papers on performance
Other relevant papers