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.
Product specifications Information source: Vendor
Last updated: Oct. 19, 2022
Product name BoneView
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
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
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
Certified, Class IIa , MDD
510(k) cleared, Class II
Market presence
On market since 03-2020
Distribution channels Incepto, Softway, Fuji Reili, Ferrum Health, Wellbeing Software, Sectra Amplifier Store, Blackford, RMS Medical Devices, AidocOS, DeepC, HCK Healthcare Konnect
Countries present (clinical, non-research use) 13 (France, Switzerland, Netherlands, Belgium, UK, Germany, Italy, Latvia, New Zealand, UAE, Spain, USA, Poland)
Paying clinical customers (institutes) 300
Research/test users (institutes) 17
Pricing model Subscription
Based on Number of users, Number of installations, Number of analyses
Peer reviewed papers on performance

  • 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