Rayvolve Trauma

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.
azmed-rayvolve
azmed-rayvolve_1
azmed-rayvolve
azmed-rayvolve_1
Information source: Vendor
Last updated: November 6, 2024

General Information

General
Product name Rayvolve Trauma
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)

Technical Specifications

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

Regulatory

Certification
CE
Certified, Class IIa , MDR
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE) Computer-aided diagnosis tool, intended to help radiologists and emergency physicians to detect and localize abnormalities on standard X-rays

Market

Market presence
On market since 06-2019
Distribution channels Blackford, Wellbeing Software, GE Edison marketplace, deepcOS, Alma AI MARKETPLACE
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Assessing the Potential of a Deep Learning Tool to Improve Fracture Detection by Radiologists and Emergency Physicians on Extremity Radiographs (read)

  • 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