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BoneView Trauma
BoneView Trauma
GLEAMER
BoneView is an AI Companion for lesion detection on Bone X-Rays. BoneView can detect fractures, effusions, dislocations and bone lesions, and gives 3 different pre-diagnosis labels on the images:
- POSITIVE when the confidence for the presence of a lesion is above 90% (plain bounding box around the region of interest)
- DOUBT when the confidence for the presence of a lesion is between 50% and 90% (dotted bounding box around the region of interest)
- NEGATIVE otherwise
BoneView provides a Summary Table for quality check and overview of AI outputs at a glance, and can perform worklist prioritization according to the different pre-diagnoses.
*In the United States, BoneView Trauma is named BoneView and has been 510k FDA-cleared for pediatrics (limbs above 2yo).
Information source:
Vendor
Last updated:
Jan. 26, 2024
General Information
Technical Specifications
Regulatory
Market
Evidence
General Information
General
Product name
BoneView Trauma
Company
GLEAMER
Subspeciality
MSK
Modality
X-ray
Disease targeted
Bone fractures, effusions, dislocations and bone lesions
Key-features
Detection of fractures, effusions, dislocations and bone lesions, worklist prioritization
Suggested use
Before: adapting worklist order, flagging acute findings
During: perception aid (prompting all abnormalities/results/heatmaps), interactive decision support (shows abnormalities/results only on demand)
After: diagnosis verification
Technical Specifications
Data characteristics
Population
Adult and pediatric patients with suspicion of fracture
Input
Bone trauma X-ray
Input format
DICOM
Output
Image annotation, pre-diagnosis
Output format
DICOM
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
510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)
Provide preliminary data for helping physicians’ diagnosis of extremity X-rays
Market
Market presence
On market since
03-2020
Distribution channels
Incepto, Softway, Fuji Reili, Ferrum Health, Wellbeing Software, Sectra Amplifier Store, Blackford, RMS Medical Devices, Aidoc aiOS, deepcOS, HCK Healthcare Konnect, Eureka Clinical AI, Siemens Healthineers
Countries present (clinical, non-research use)
22
Paying clinical customers (institutes)
420
Research/test users (institutes)
22
Pricing
Pricing model
Subscription
Based on
Number of users, Number of installations, Number of analyses
Evidence
Evidence
Peer reviewed papers on performance
Diagnostic power of ChatGPT 4 in distal radius fracture detection through wrist radiographs
(read)
Radiographic Detection of Post-Traumatic Bone Fractures: Contribution of Artificial Intelligence Software to the Analysis of Senior and Junior Radiologists
(read)
AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice
(read)
Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception - A Prospective Pre- and Post-Analysis
(read)
Commercially-available AI algorithm improves radiologists’ sensitivity for wrist and hand fracture detection on X-ray, compared to a CT-based ground truth
(read)
Artificial Intelligence for Detecting Acute Fractures in Patients Admitted to an Emergency Department: Real-Life Performance of Three Commercial Algorithms
(read)
Artificial intelligence vs. radiologist: accuracy of wrist fracture detection on radiographs
(read)
A Prospective Approach to Integration of AI Fracture Detection Software in Radiographs into Clinical Workflow
(read)
Assessment of an artificial intelligence aid for the detection of appendicular skeletal fractures in children and young adults by senior and junior radiologists
(read)
Added value of an artificial intelligence solution for fracture detection in the radiologist’s daily trauma emergencies workflow
(read)
Assessment of performances of a deep learning algorithm for the detection of limbs and pelvic fractures, dislocations, focal bone lesions, and elbow effusions on trauma X-rays
(read)
Automated detection of acute appendicular skeletal fractures in pediatric patients using deep learning
(read)
Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence
(read)
Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study
(read)
Non-peer reviewed papers on performance
Other relevant papers