b-box plus

b-rayZ

Stand-alone AI system for assessment of mammographic breast density, real-time evaluation of image quality, lesion detection and mechanism for personalized suggestion of supplemental diagnostics.

Information source: Vendor
Last updated: Jan. 23, 2024

General Information

General
Product name	b-box plus
Company	b-rayZ
Subspeciality	Breast
Modality	Mammography
Disease targeted	Breast cancer
Key-features	Breast density classification (ACR BI-RADS), real time image quality assessment, lesion and microcalcification detection, population summary dashboard
Suggested use	During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand), report suggestion After: diagnosis verification

Technical Specifications

Data characteristics
Population	Asymptomatic women
Input	2D Full-Field Digital Mammography, 3D Digital Breast Tomosynthesis
Input format	DICOM
Output	ACR density class, diagnostic quality classification, BI-RADS compliant classification, region findings
Output format	DICOM SC, DICOM SR
Technology
Integration	Integration in standard reading environment (PACS), Stand-alone third party application
Deployment	Locally virtualized (virtual machine, docker)
Trigger for analysis	Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time	10 - 60 seconds

Regulatory

Certification
CE	Certified, Class IIa , MDR
FDA	No or not yet
Intended Use Statements
Intended use (according to CE)	The b-box plus is a stand-alone software intended to determine the breast density classification from mammography data and to classify mammography data with respect to their diagnostic quality and pathological abnormalities. The results are provided to the qualified medical user (the radiologist) who shall decide about further steps, such as the acquisition of new images or the supplementation with other imaging modalities (e.g. ultrasound). Qualified medical users shall review, and release the outcome of the software. If necessary, the qualified medical users shall edit the software outcome before release. After release, the findings can then be directly translated to a compatible history file for the Institutional Picture Archiving-System (PAC-System), and presented in a statistical overview (dashboard) accessible to the qualified medical users.

Market

Market presence
On market since	05-2020
Distribution channels	RMS Medical Devices, Blackford
Countries present (clinical, non-research use)	7
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing
Pricing model	Subscription
Based on	Number of analyses

Evidence

Evidence
Peer reviewed papers on performance	Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks (read) Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network (read) BI-RADS-Based Classification of Mammographic Soft Tissue Opacities Using a Deep Convolutional Neural Network (read) Classification of Mammographic Breast Microcalcifications Using a Deep Convolutional Neural Network (read) Determination of mammographic breast density using a deep convolutional neural network (read)
Non-peer reviewed papers on performance
Other relevant papers