RBknee

by Radiobotics ApS  · Based in Denmark → — Expert-level care for every patient, every time.
Orthopedics Radiology Rheumatology
Website
Claim this tool
Is this the maker? Claim the company →

Contact for pricing
Regulatory Status Disclosed

Overview

RBknee by Radiobotics ApS is an AI-powered software designed to assist healthcare professionals in the radiographic analysis and reporting of knee osteoarthritis (OA) from X-ray images. The fully-automated image processing stand-alone software can detect findings relevant to radiographic osteoarthritis, such as joint space narrowing, osteoarthritis grading, and osteophytes. It provides a visual overlay and a structured text report with findings and conclusions, aiming to improve diagnostic accuracy and efficiency. RBknee also measures the joint space width in both compartments of the knee. Studies have shown that RBknee can improve consistency and performance in Kellgren-Lawrence (KL) grading, especially among junior readers, and increase interobserver agreement.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automated detection of radiographic osteoarthritis findings
  • Joint space narrowing detection
  • Osteoarthritis grading (e.g., Kellgren-Lawrence system)
  • Osteophyte detection
  • Subchondral sclerosis identification
  • Measures joint space width
  • Generates visual overlays on X-ray images
  • Provides structured text reports with findings and conclusions
  • Processing time less than 10 seconds

Use Cases

  • Radiographic analysis and reporting of knee osteoarthritis
  • Assisting healthcare professionals in diagnosing knee OA
  • Improving diagnostic accuracy and efficiency in MSK imaging
  • Enhancing interobserver agreement among radiologists and orthopedists
  • Standardizing the assessment pathway for knee osteoarthritis patients
  • Reducing unnecessary MRI examinations

What Physicians Need to Know

Evidence Base
RBknee is designed to automatically detect and report findings relevant for radiographic knee osteoarthritis (OA), including joint space narrowing, osteophytes, and OA grading based on Kellgren-Lawrence (KL) and OARSI criteria. Its development is supported by peer-reviewed publications on external validation and consistency of OA grading.
Clinical Validation Studies
RBknee has received FDA 510(k) clearance (Class II) and CE Mark certification (Class IIa under MDR). Rigorous testing and clinical validation have demonstrated high sensitivity and specificity in identifying findings and high precision in measurements. Studies show high agreement between the AI tool and radiologists, with one external validation study reporting 'similar and almost perfect agreement' with musculoskeletal radiology consultant consensus. It has been shown to improve the accuracy and consistency of knee OA grading, particularly among junior readers, with board-certified radiologists achieving near-perfect agreement when assisted by RBknee.
Clinical Workflow Integration
RBknee integrates into standard reading environments, including PACS and RIS, and can be deployed via AI marketplaces or distribution platforms. It supports both cloud-based and locally virtualized (VM, Docker) deployments. Analysis can be triggered automatically after image acquisition or on demand, providing a visual overlay and a draft radiology report in DICOM format for review. This integration aims to provide speedy, expert-level reporting and potentially reduce unnecessary MRI examinations.
Physician Tip

RBknee serves as a valuable adjunctive tool for the radiographic analysis and reporting of knee osteoarthritis. Physicians can leverage its automated detection of key OA findings (joint space narrowing, osteophytes, sclerosis) and Kellgren-Lawrence grading to enhance diagnostic consistency and efficiency. It is particularly beneficial for standardizing reporting and improving interobserver agreement, especially among less experienced readers. While highly accurate for OA, remember it is specialized for this condition and should be used in conjunction with full patient evaluation. Its ability to provide prompt, objective analysis may aid in faster patient management and potentially optimize resource allocation by reducing unnecessary advanced imaging.

RBknee is designed for seamless integration into existing radiology workflows. It is compatible with standard PACS and RIS, allowing for automated or on-demand analysis of DICOM images. The output, including visual overlays and structured reports, is also in DICOM format, ensuring it can be easily viewed and incorporated within your current imaging review platforms. Deployment flexibility (cloud or on-premise via Docker) ensures adaptability to various IT infrastructures.

Details

Category Clinical Decision Support & Reference, Radiology & Imaging AI
Pricing Contact for pricing
DeploymentCloud-based; Locally virtualized (virtual machine, Docker); Integration in standard reading environment (PACS); Integration RIS (Radiological Information System); Integration via AI marketplace or distribution platform.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

RBknee received U.S. Food and Drug Administration (FDA) 510(k) clearance (K203696) on August 27, 2021, as a Class II medical device.
Integrations
EHR Not specified
Specialties Orthopedics, Radiology, Rheumatology

What the Web Says

RBknee is an AI-powered software designed to assist healthcare professionals in analyzing and reporting on knee osteoarthritis (OA) from X-rays. Studies indicate that RBknee can improve the accuracy and consistency of Kellgren-Lawrence (KL) grading for knee OA, particularly among junior readers, and can achieve agreement levels comparable to or even surpassing those of experienced radiologists. The tool aims to provide prompt diagnostics and standardized reporting for knee osteoarthritis.

Overall: Positive

Strengths

  • Improves interobserver agreement for KL grading among all readers, especially junior readers.
  • Achieves high agreement with musculoskeletal specialists for osteoarthritis grading.
  • Provides prompt, same-day diagnostics to patients.
  • Offers comprehensive analysis of osteoarthritis indicators like joint space narrowing, osteophytes, and subchondral sclerosis.
  • Reduces the need for unnecessary MRI examinations.
  • Facilitates standardized reporting for knee osteoarthritis.

Limitations

  • May reflect human biases present in the training data, with some inconsistencies in grading between left and right knees (15-20% of cases).
  • One study noted a case where the AI tool graded a knee two or more points lower than the radiology consultant consensus.
  • No specific reviews from G2, Capterra, or Reddit directly discussing RBknee were found, making it difficult to assess user-reported cons from these platforms.

Based on reviews from: Radiobotics, Health AI Register, AuntMinnie, TestDynamics, AuntMinnieEurope, RSNA

Last updated: 2026-07-20

Ratings & Reviews

No reviews yet. Be the first to review this tool!

Rate RBknee

Clinical Value
Ease of Use
Integration
Support & Docs
Value for Money

Press & Coverage

AuntMinnie
Radiobotics gets FDA clearance for knee OA algorithm
Radiobotics received U.S. Food and Drug Administration 510(k) clearance for RBknee, an AI-based software application designed to aid in the diagnosis of knee osteoarthritis (OA) on radiography. The software analyzes digital x-rays to identify common radiographic findings associated with OA.
2021-08
Radiobotics
RBfractureu2122 receives MDR certification
Radiobotics announced that its product RBknee has been CE marked under the MDR certification as a class IIa medical device in the European Union. This certification ensures patient safety and compliance with high European standards for medical devices and software providing decision support.
2021-06
PR Newswire
TeleRay and Radiobotics Join Forces to Provide Radiology AI on Fastest Growing Radiology Telehealth Platform
TeleRay partnered with Radiobotics to integrate RBknee, an FDA-cleared clinical decision support tool for knee osteoarthritis detection, into the TeleRay platform. This collaboration aims to provide TeleRay users with AI-powered analysis of knee x-rays.
2021-12
AuntMinnie
AI improves evaluations of knee osteoarthritis on x-rays
A study published in Radiology found that a commercially available AI algorithm, RBknee, improved the performance of junior radiologists in grading knee osteoarthritis on x-rays and increased interobserver agreement among all readers. The study involved 225 standing knee x-rays from three European centers.
2024-07
Radiology (RSNA Journals)
Interobserver Agreement and Performance of Concurrent AI Assistance for Radiographic Evaluation of Knee Osteoarthritis
This peer-reviewed study evaluated the impact of RBknee (version 2.1) on the accuracy and agreement among radiologists and orthopedists in grading knee osteoarthritis. The findings indicated that AI assistance improved the Kellgren-Lawrence grading performance of junior readers and increased interobserver agreement.
2024-07
AuntMinnie
Blackford, Radiobotics enter commercial partnership
Blackford and Radiobotics announced a commercial partnership to integrate RBfracture and RBknee into the Blackford Platform. This collaboration aims to provide healthcare professionals with advanced AI tools for automated fracture detection and knee osteoarthritis analysis.
2024-03
Radiobotics
Radiobotics partners with deepc to integrate their innovative AI tools into the deepcOS platform
Radiobotics has partnered with deepc to integrate its AI tools, including RBknee, into the deepcOS platform. This integration will enhance deepcOS capabilities, offering healthcare professionals state-of-the-art tools for improved patient outcomes without disrupting current workflows.
2024-05
Radiobotics
AI tools trained on human-labeled data reflect human biases
This peer-reviewed publication in Nature Scientific Reports, published in November 2024, assesses the consistency of RBknee's osteoarthritis grading and its ability to detect side-to-side differences using a large clinical dataset. The study highlights that AI tools trained on human-labeled data can reflect human biases.
2024-11

Videos

Product demos, reviews, and walkthroughs for RBknee.

View all on YouTube

Frequently Asked Questions

RBknee is designed for integration into standard reading environments such as Picture Archiving and Communication Systems (PACS) and Radiology Information Systems (RIS), as well as through AI marketplaces or distribution platforms. It supports both cloud-based and locally virtualized deployments, with analysis triggered automatically after image acquisition or on demand by a user.
RBknee analyzes digital X-rays of knees to identify common radiographic findings associated with osteoarthritis, including osteophytes, subchondral sclerosis, and joint space narrowing. It also provides Kellgren-Lawrence osteoarthritis grading and measures joint space width, generating a detailed descriptive report and visual overlay.
RBknee has received U.S. Food and Drug Administration (FDA) 510(k) clearance as a Class II device. Additionally, it holds the CE Mark, certified as Class IIa in accordance with the (EU) Medical Device Regulation (MDR) 2017/745 for use in Europe.
As an FDA-cleared and CE-marked medical device, RBknee is expected to adhere to stringent healthcare data protection regulations such as HIPAA and GDPR. It processes DICOM input and output, typically operating within secure, integrated healthcare IT environments to maintain data privacy and security.
The primary alternative to an AI tool like RBknee is the traditional manual interpretation and reporting of knee X-rays by radiologists or orthopedic specialists. Other non-AI diagnostic methods and comprehensive clinical assessments also remain crucial components in the overall diagnosis and management of knee osteoarthritis.
The publicly available search results do not specify the pricing model for RBknee. Information regarding subscription, per-use, or various licensing options would typically need to be obtained directly from Radiobotics or their authorized distributors.
While studies indicate high agreement between RBknee and radiologists, AI tools trained on human-labeled data can reflect human biases, with RBknee occasionally grading left and right knees differently in a small percentage of cases. Its performance has been validated on typical weight-bearing X-rays, even though it was trained on fixed-flexion images.
RBknee is intended as an adjunctive tool to assist healthcare professionals in the radiographic analysis and reporting of knee osteoarthritis. It should not be used as a standalone diagnostic tool or be solely relied upon to make or confirm a diagnosis, emphasizing its role as a supportive aid.

Related Tools

Cortex
Cortex
Clinical Decision Support & Reference
Cortex Workspace is a secure, HIPAA-compliant AI platform designed to streamline healthcare workflows and enhance decision-making for physicians.
Starts free, then $29/month for individual plan (save 10% annually). other plans: enthusiast ($99/month), business ($349/month), enterprise (contact sales).
SureMediks ProView
SureMediks (Rasimo Systems, LLC)
Clinical Decision Support & Reference
SureMediks is an AI-powered clinical decision support and metabolic health platform that automates care workflows for specialists, providing AI-generated patient summaries, automated chart review, and real-time biometric alerts.
SOAP Health
SOAP Health
Clinical Decision Support & Reference
SOAP Health is a Florida-based startup transforming patient intake with AI-powered diagnosis, offering a comprehensive solution that improves diagnosis accuracy and empowers patients and physicians.
Oxipit ChestLink
Oxipit (part of Sectra group)
Clinical Decision Support & Reference
Oxipit ChestLink is the world's first CE Class IIb-certified autonomous AI application for healthy chest X-ray reporting, capable of identifying normal studies with 99.9% sensitivity. It automates the reporting of normal chest X-rays, reducing radiologist workload and improving efficiency.
KATE AI
Mednition
Clinical Decision Support & Reference
KATE AI is a nurse-built clinical decision support tool for emergency departments that analyzes incoming patients in real-time to flag high-risk cases that standard acuity scoring might miss. It has supported over 5,000,000 patient visits, helping emergency teams deliver safer, faster, and more informed care.
Fee-based, not publicly disclosed.
MCG Health
MCG Health
Clinical Decision Support & Reference
MCG Health provides industry-leading evidence-based guidance and technologies to optimize health outcomes, drive operational efficiency, and achieve cost savings. Their solutions are utilized by thousands of hospitals, a majority of U.S. health plans, and various government health programs.
Contact for pricing

See all Clinical Decision Support & Reference tools →

Suggest an Edit → | Last Verified: 2026-04-20 | First Added: 2026-04-20
AI Tool Finder
AI-powered search. Results may not be comprehensive.