BoneMetrics

GLEAMER
BoneMetrics provides automated MSK measurements for the spine, pelvis, feet, shoulder, knee and legs, on X-ray and EOS acquisitions.
gleamer-ai-boneview-measurements
gleamer-ai-boneview-measurements_1
gleamer-ai-boneview-measurements
gleamer-ai-boneview-measurements_1
Information source: Vendor
Last updated: March 3, 2025

General Information

General
Product name BoneMetrics
Company GLEAMER
Subspeciality MSK
Modality X-ray
Disease targeted Spine deformities, scoliosis, pelvic obliquity, leg length discrepancy, femoral coverage, acetabular impingement, hip dysplasia, hip arthritis, genu varum, genu valgum, patella alta/baja
Key-features Automated measurements of spine, pelvis, feet, and legs
Suggested use During: report suggestion
Without interference of a radiologist: AI-only diagnosis

Technical Specifications

Data characteristics
Population Adults and children (>3 years old for legs and spine / >10 years old for foot and pelvis)
Input 2D radiograph or EOS
Input format DICOM
Output A structured report and overlay of the measurements on the DICOM
Output format DICOM Secondary Capture
Technology
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System)
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 Measurements 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 AGFA, Aidoc, Blackford, deepcOS, Incepto, Sectra, Eureka Clinical AI
Countries present (clinical, non-research use) >20
Paying clinical customers (institutes) >200
Research/test users (institutes)
Pricing
Pricing model Subscription
Based on Number of analyses

Evidence

Evidence
Peer reviewed papers on performance

  • Evaluation of a deep learning software for automated measurements on full-leg standing radiographs (read)

  • Deep learning algorithm enables automated Cobb angle measurements with high accuracy (read)

  • Validation of AI-driven measurements for hip morphology assessment (read)

  • Automated weight-bearing foot measurements using an artificial intelligence-based software (read)
Non-peer reviewed papers on performance

  • Abstract ECR 2023 (RPS 705-6): Automated hip measurements using an artificial intelligence-based software (read)

  • Abstract ECR 2023 (RPS 705-7): Automated full-leg measurements using an artificial intelligence-based software (read)

  • Poster ESSR 2023 (#317): Automated Measurements of Cobb Angle on Full-Spine Radiographs Reach High Accuracy (read)


  • Poster ESSR 2023 (#219): Automated Full-Leg Measurements Using an Artificial Intelligence-Based Software (read)


  • Poster ESSR 2023 (#218): Automated Hip Measurements Using an Artificial Intelligence-Based Software (read)

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    Other relevant papers