This platform uses computer vision and AI to analyze patient movements in videos, providing objective metrics of Parkinson's symptoms like tremors and bradykinesia. The technology helps researchers track and score motor assessments for Parkinson's patients.
Funding
Funding not disclosed
Founders
Product
Problem
Current methods for assessing motor function in Parkinson's disease often rely on subjective clinical observations, leading to variability in scoring and challenges in tracking subtle changes in symptoms over time. Traditional motor assessments can also be time-consuming and require specialized training, limiting their scalability and accessibility for frequent monitoring.
Solution
Machine Medicine offers Kelvin, an AI-powered platform that analyzes patient movements in videos to provide objective, quantitative metrics of Parkinson's disease symptoms. Using computer vision and machine learning, Kelvin automatically tracks and scores motor assessments, reducing subjectivity and improving the reliability of evaluations. The platform enables researchers and clinicians to monitor disease progression, assess treatment response, and personalize care based on data-driven insights. By automating the motor assessment process, Machine Medicine aims to simplify and scale access to objective measures of motor function for Parkinson's patients.
Target Audience
The primary users are clinical researchers and healthcare providers specializing in Parkinson's disease who require objective and scalable tools for motor assessment.
Features
- AI-driven video analysis for automated scoring of motor assessments
- Objective metrics of Parkinson's symptoms, including tremor and bradykinesia
- Longitudinal tracking of motor function to monitor disease progression
- Secure data management compliant with ISO 27001:2022 standards