BoneView

GLEAMER
BoneView detects 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 has been 510k FDA-cleared for pediatric use and adults (>2 years old for limbs).
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Information source: Vendor
Last updated: October 29, 2024

General Information

General
Product name BoneView
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)
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, GSPS, DICOM SR
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 AGFA, Aidoc, Blackford, Carpl.ai, deepcOS, Ferrum, Fujifilm, Incepto, Microsoft (Nuance Communications), Sectra Amplifier Store, Siemens Healthineers, Eureka Clinical AI
Countries present (clinical, non-research use) >40
Paying clinical customers (institutes) >650
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of users, Number of installations, Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Assessment of an AI Aid in Detection of Adult Appendicular Skeletal Fractures by Emergency Physicians and Radiologists: A Multicenter Cross-sectional Diagnostic Study (read)

  • Effectiveness of an Artificial Intelligence Software for Limb Radiographic Fracture Recognition in an Emergency Department (read)

  • AI-assisted X-ray Fracture Detection in Residency Training: Evaluation in Pediatric and Trauma Patients (read)

  • 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)

  • 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

  • Implementing Artificial Intelligence for Emergency Radiology Impacts Physicians' Knowledge and Perception - A Prospective Pre- and Post-Analysis (read)

  • Abstract ECR 2024: Implementation of an AI Application for Fracture Detection: Major benefits for Patients and Healthcare Workers (read)