Radiobotics develops machine learning algorithms for trauma fracture detection in medical imaging, achieving 94% accuracy and a median processing time of 13 seconds per exam. Their solution significantly reduces missed fracture rates by 86%, ensuring timely and precise care for patients in emergency departments and radiology settings.
Funding
$6.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Emergency departments and radiology settings face challenges in the timely and accurate detection of trauma fractures, potentially leading to delayed treatment and increased costs. Traditional fracture detection methods can be time-consuming and prone to human error, impacting patient outcomes.
Solution
Radiobotics offers RBfracture, an AI-powered solution designed to automatically detect trauma fractures in medical imaging. Trained on emergency data from over 1,300 hospitals worldwide, RBfracture achieves high accuracy, specificity, and sensitivity in identifying fractures, dislocations, lipohemarthrosis, and effusions. The solution seamlessly integrates into existing radiology workflows, providing clinicians with rapid and reliable decision support. By reducing missed fracture rates and accelerating diagnosis, RBfracture aims to improve patient care, reduce financial burdens, and increase patient satisfaction.
Target Audience
The primary target audience includes radiologists, emergency department physicians, and healthcare providers in hospitals and radiology clinics seeking to improve the speed and accuracy of fracture detection.
Features
- AI-powered detection of fractures, dislocations, lipohemarthrosis, and effusions in both pediatric and adult patients
- Trained on emergency data from over 1,300 hospitals globally
- Achieves 94% accuracy, specificity, and sensitivity
- Median processing time of 13 seconds per exam
- Reduces missed fracture rates by 86%
- Seamless integration into existing Picture Archiving and Communication Systems (PACS)
- Implementation options that adapt to existing workflows