RPI ID: 2020-105-201

Innovation Summary:
A predictive method is introduced for assessing bone fracture risk in patients with type 2 diabetes using a combination of clinical data and imaging biomarkers. The system analyzes bone quality metrics beyond bone mineral density, incorporating trabecular patterns and cortical thickness. Machine learning algorithms are applied to stratify risk levels and generate individualized assessments. The method is designed to improve early intervention and treatment planning for diabetic patients at elevated fracture risk.

Challenges / Opportunities:
Standard fracture risk tools often underestimate risk in diabetic populations due to atypical bone characteristics. This invention addresses that gap by integrating diabetes-specific bone metrics and predictive modeling. It opens opportunities for personalized care and improved outcomes in endocrinology and orthopedics. The approach supports integration with electronic health records and diagnostic imaging systems.

Key Benefits / Advantages:
✔ Diabetes-specific fracture risk assessment
✔ Incorporates advanced bone imaging biomarkers
✔ Machine learning-based prediction
✔ Supports early intervention strategies
✔ Compatible with clinical workflows

Applications:
• Endocrinology
• Orthopedic risk screening
• Preventive care in diabetes management

Keywords:
#fracturerisk #type2diabetes #bonehealth #predictiveanalytics #medicalimaging #clinicaldecisiontools

Intellectual Property:
US Application 18/216679, US20240047071A1 filed 30-Jun-2023