BonePixel provides a clinical decision support platform that utilizes machine learning algorithms and a comprehensive historical database to analyze clinical data for joint disorders. Its first product, VirtualHip, offers a fully automated 3D assessment of hip abnormalities, delivering personalized treatment recommendations based on patient-specific data.
Funding
$100K 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
Existing clinical tools for analyzing joint injuries often lack the sensitivity needed for patient-specific diagnosis and treatment planning. Current approaches to analyzing examination results and diagnostic images are burdensome, time-consuming, and fail to fully leverage available data.
Solution
BonePixel offers a clinical decision support platform that uses machine learning algorithms and a comprehensive historical database to provide data-driven insights for personalized diagnosis and treatment planning of joint injuries. The platform's first product, VirtualHip, delivers a fully automated 3D assessment of hip abnormalities. By matching new patients to previously treated patients, BonePixel assists with personalized diagnosis and treatment planning. The technology aims to provide clinicians with additional insights required for patient-specific diagnosis and treatment planning, with the ultimate goal of improving treatment outcomes.
Target Audience
The primary users are clinicians treating patients with joint injuries, specifically those focused on hip preservation in youth and young adults.
Features
- Fully automated analysis of morphology, structural properties, and function from clinical images.
- Advanced matching algorithms to match new patients to previously treated patients.
- Comprehensive historical database of normal and pathologic joints from children, adolescents, and adults.
- Patient-specific treatment suggestions based on comprehensive historical data and clinically trained AI.
- Validated algorithms for fully automated evaluation of hip joint morphology in 3D with normative data.
- Dynamic evaluation of hip impingement and instability under a personalized range of motion.
- AI-assisted platform for personalized diagnosis and treatment planning.