RBknee

JSON twin: https://www.healthaidb.com/software/rbknee.json

Company Name

Radiobotics

Product URL

https://radiobotics.com/products/rbknee

Company URL

https://radiobotics.com

Categories

• diagnostic Support
• clinical Care
• radiology
• Diagnostic
• Clinical
• Musculoskeletal

Summary

RBknee is an AI-powered software developed by Radiobotics that automates the analysis of knee X-rays to assist in diagnosing knee osteoarthritis, providing objective measurements and standardized grading to support medical professionals in evaluation and treatment planning.

Description

RBknee utilizes machine learning algorithms to analyze digital knee X-rays, identifying radiographic signs associated with osteoarthritis, such as joint space narrowing, osteophytes, and subchondral sclerosis. It measures joint space width in both knee compartments and provides standardized grading based on Kellgren-Lawrence criteria, aiding clinicians in accurate diagnosis and treatment decisions.

Api Available

unknown

Certifications

• FDA 510(k)
• CE/MDR
• ISO 13485

Company Founding

2017

Company Offices

• Denmark
• United States

Compliance

• HIPAA
• GDPR
• HITECH
• SOC 2
• ISO 27001

Customers

• Kettering General Hospital Foundation Trust

Data Residency

US/EU regions

Data Standards

• DICOM

Deployment Model

• SaaS
• on_prem
• hybrid

Features

• Automated detection of knee osteoarthritis (OA) on X-rays
• Identification of osteophytes, subchondral sclerosis, and joint space narrowing
• Generation of OA grading reports based on the Kellgren-Lawrence scale
• Integration with standard reading environments (PACS)
• Integration with Radiological Information Systems (RIS)
• Integration via AI marketplaces or distribution platforms
• Deployment options: locally virtualized (virtual machine, Docker) or cloud-based
• Analysis triggered automatically post-image acquisition or on-demand by user
• Processing time: less than 3 seconds per image
• Secondary capture with annotated regions of interest highlighting OA signs
• CE-marked as Class IIa medical device under MDR
• FDA 510(k) cleared as Class II medical device
• Intended for use as an adjunctive tool to assist healthcare professionals in radiographic analysis and reporting on knee OA from lateral and weight-bearing anterior-posterior or posterior-anterior projection radiographs of unilateral or bilateral knees

Id

SW0453

Integration Partners

• Aarhus University Hospital

Integrations

• PACS
• RIS
• AI marketplaces
• distribution platforms

Languages Supported

• English
• Danish

Last Updated

2025-10-11

License

commercial

Market Segment

• enterprise
• smb

Optional Modules

Os Platforms

• Web
• iOS
• Android
• Windows
• macOS
• Linux

Pricing Details

Contact vendor for pricing information.

Pricing Model

subscription

Privacy Features

• BAA available
• consent mgmt
• anonymization
• data minimization

Product Code

SW0453

Product Name

RBknee

Ratings

• 94% accuracy, specificity, and sensitivity
• 86% reduction in missed fracture rate
• 13s median processing time per exam

Regions Available

• United States
• European Union

Related Urls

• https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices

Release Year

2024

Security Features

• Encryption
• RBAC
• SSO/SAML
• audit logs
• 2FA
• DLP

Specialties

• Orthopedics
• Radiology
• Musculoskeletal Disorders

Support Channels

• email
• phone
• chat
• ticketing
• community
• 24x7

System Requirements

Major OS/DB/hardware needs, or empty if SaaS-only

Target Users

• clinicians
• radiologists
• orthopedic surgeons

Training Options

• documentation
• webinars
• live_online
• onsite
• certification

Type

product

User Reviews

• I never thought AI solutions for fracture detection would be useful for an experienced reporter, but RBfracture caught several fractures that I would have otherwise missed.

Version

1.0

Alternatives

See related products

Canonical JSON

{
  "product_name": "RBknee",
  "company_name": "Radiobotics",
  "product_url": "https://radiobotics.com/products/rbknee",
  "company_url": "https://radiobotics.com",
  "related_urls": [
    "https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices"
  ],
  "product_code": "SW0453",
  "summary": "RBknee is an AI-powered software developed by Radiobotics that automates the analysis of knee X-rays to assist in diagnosing knee osteoarthritis, providing objective measurements and standardized grading to support medical professionals in evaluation and treatment planning.",
  "description": "RBknee utilizes machine learning algorithms to analyze digital knee X-rays, identifying radiographic signs associated with osteoarthritis, such as joint space narrowing, osteophytes, and subchondral sclerosis. It measures joint space width in both knee compartments and provides standardized grading based on Kellgren-Lawrence criteria, aiding clinicians in accurate diagnosis and treatment decisions.",
  "categories": [
    "diagnostic Support",
    "clinical Care",
    "radiology",
    "Diagnostic",
    "Clinical",
    "Radiology",
    "Musculoskeletal"
  ],
  "market_segment": [
    "enterprise",
    "smb"
  ],
  "target_users": [
    "clinicians",
    "radiologists",
    "orthopedic surgeons"
  ],
  "specialties": [
    "Orthopedics",
    "Radiology",
    "Musculoskeletal Disorders"
  ],
  "regions_available": [
    "United States",
    "European Union"
  ],
  "languages_supported": [
    "English",
    "Danish"
  ],
  "pricing_model": "subscription",
  "pricing_details": "Contact vendor for pricing information.",
  "license": "commercial",
  "company_offices": [
    "Denmark",
    "United States"
  ],
  "company_founding": "2017",
  "deployment_model": [
    "SaaS",
    "on_prem",
    "hybrid"
  ],
  "os_platforms": [
    "Web",
    "iOS",
    "Android",
    "Windows",
    "macOS",
    "Linux"
  ],
  "features": [
    "Automated detection of knee osteoarthritis (OA) on X-rays",
    "Identification of osteophytes, subchondral sclerosis, and joint space narrowing",
    "Generation of OA grading reports based on the Kellgren-Lawrence scale",
    "Integration with standard reading environments (PACS)",
    "Integration with Radiological Information Systems (RIS)",
    "Integration via AI marketplaces or distribution platforms",
    "Deployment options: locally virtualized (virtual machine, Docker) or cloud-based",
    "Analysis triggered automatically post-image acquisition or on-demand by user",
    "Processing time: less than 3 seconds per image",
    "Secondary capture with annotated regions of interest highlighting OA signs",
    "CE-marked as Class IIa medical device under MDR",
    "FDA 510(k) cleared as Class II medical device",
    "Intended for use as an adjunctive tool to assist healthcare professionals in radiographic analysis and reporting on knee OA from lateral and weight-bearing anterior-posterior or posterior-anterior projection radiographs of unilateral or bilateral knees"
  ],
  "optional_modules": [],
  "integrations": [
    "PACS",
    "RIS",
    "AI marketplaces",
    "distribution platforms"
  ],
  "data_standards": [
    "DICOM"
  ],
  "api_available": "unknown",
  "system_requirements": "Major OS/DB/hardware needs, or empty if SaaS-only",
  "compliance": [
    "HIPAA",
    "GDPR",
    "HITECH",
    "SOC 2",
    "ISO 27001"
  ],
  "certifications": [
    "FDA 510(k)",
    "CE/MDR",
    "ISO 13485"
  ],
  "security_features": [
    "Encryption",
    "RBAC",
    "SSO/SAML",
    "audit logs",
    "2FA",
    "DLP"
  ],
  "privacy_features": [
    "BAA available",
    "consent mgmt",
    "anonymization",
    "data minimization"
  ],
  "data_residency": "US/EU regions",
  "customers": [
    "Kettering General Hospital Foundation Trust"
  ],
  "user_reviews": [
    "I never thought AI solutions for fracture detection would be useful for an experienced reporter, but RBfracture caught several fractures that I would have otherwise missed."
  ],
  "ratings": [
    "94% accuracy, specificity, and sensitivity",
    "86% reduction in missed fracture rate",
    "13s median processing time per exam"
  ],
  "support_channels": [
    "email",
    "phone",
    "chat",
    "ticketing",
    "community",
    "24x7"
  ],
  "training_options": [
    "documentation",
    "webinars",
    "live_online",
    "onsite",
    "certification"
  ],
  "release_year": "2024",
  "integration_partners": [
    "Aarhus University Hospital"
  ],
  "id": "SW0453",
  "slug": "rbknee",
  "type": "product",
  "version": "1.0",
  "last_updated": "2025-10-11",
  "links_json": {
    "self": "https://www.healthaidb.com/software/rbknee.json"
  }
}