b-box

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.
Product specifications Information source: Vendor
Last updated: Nov. 8, 2022
General
Product name b-box
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
Data characteristics
Population Women of all ethnicities
Input 2D mammography images or C-View images (2D tomosynthesis reconstructions)
Input format DICOM
Output Segmentation overlays of lesions and microcalcifications, quality rating, breast density classification, patient population summary dashboard
Output format DICOM encapsulated image or DICOM SR
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), Hybrid solution
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
Certification
CE
Certified, Class IIa , MDD
FDA
 No or not yet
Market presence
On market since 05-2020
Distribution channels
Countries present (clinical, non-research use) 3
Paying clinical customers (institutes) 6
Research/test users (institutes) 6
Pricing
Pricing model Subscription
Based on Number of analyses
Evidence
Peer reviewed papers on performance

  • 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