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BoneView Bone Age
BoneView Bone Age
GLEAMER
The algorithm automates bone age assessment according to the Greulich & Pyle atlas. It aims to overcome the problem of considerable reader variability of manual ratings.
Information source:
Vendor
Last updated:
Sept. 14, 2023
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
General
Product name
BoneView Bone Age
Company
GLEAMER
Subspeciality
MSK
Modality
X-ray
Disease targeted
Early or late puberty, Congenital Adrenal Hyperplasia (CAH), orthopedic treatment planning, sports medicine, clinical trials
Key-features
Bone age
Suggested use
During: perception aid (prompting all abnormalities/results/heatmaps)
After: diagnosis verification
Technical Specifications
Data characteristics
Population
Children from 3 to 17 years old
Input
Posterior anterior (PA) hand radiograph
Input format
DICOM
Output
Greulich&Pyle bone age, standard deviation
Output format
DICOM-encapsulated report
Technology
Integration
Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform
Deployment
Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis
Automatically, right after the image acquisition
Processing time
1 - 10 minutes
Regulatory
Certification
CE
Certified, Class IIa
, MDR
FDA
No or not yet
Intended Use Statements
Intended use (according to CE)
BoneView Bone Age is a software using deep learning techniques intended to provide preliminary data for helping clinicians’ diagnosis of X-ray radiographs.
Market
Market presence
On market since
03-2023
Distribution channels
Eureka Clinical AI
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
19
Research/test users (institutes)
Pricing
Pricing model
Subscription
Based on
Number of analyses
Evidence
Evidence
Peer reviewed papers on performance
High performance for bone age estimation with an artificial intelligence solution
(read)
Non-peer reviewed papers on performance
Abstract ESPR 2022: Deep learning algorithm to predict Greulich and Pyle bone age
(read)
Other relevant papers