VUNO Med®-DeepBrain™

VUNO

It parcellates the brain into 100+ parts using 3D T1 non-contrast MRI and provides quantitative data describing volume, normative percentiles, and cortical thickness with color overlays. The solution assists the diagnosis of neurodegenerative disorders by analyzing the atrophy of main brain structures.

Information source: Vendor
Last updated: November 19, 2024

General Information

General
Product name	VUNO Med®-DeepBrain™
Company	VUNO
Subspeciality	Neuro
Modality	MR
Disease targeted	Atrophy, Dementia
Key-features	Brain parcellation, Atrophy quantification
Suggested use	During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion

Technical Specifications

Data characteristics
Population	Population with a risk of brain atrophy
Input	3D T1 Brain MRI
Input format	DICOM
Output	Parcellation of whole brain in 100 parts, Quantification of atrophy, Normal comparison
Output format	DICOM, NIFTI, PDF
Technology
Integration	Integration in standard reading environment (PACS), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment	Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based, Hybrid solution
Trigger for analysis	Automatically, right after the image acquisition
Processing time	10 - 60 seconds

Regulatory

Certification
CE	Certified, Class IIa , MDD
FDA	510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)

Market

Market presence
On market since	06-2020
Distribution channels
Countries present (clinical, non-research use)	10+
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model	Pay-per-use
Based on	Number of analyses

Evidence

Evidence
Peer reviewed papers on performance	Automated idiopathic normal-pressure hydrocephalus diagnosis via artificial intelligence-based 3D T1 MRI volumetric analysis (read) Impact of white matter hyperintensity volumes estimated by automated methods using deep learning on stroke outcomes in small vessel occlusion stroke (read)
Non-peer reviewed papers on performance
Other relevant papers