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Background: The prevention of lower extremity amputations in patients with Peripheral Artery Disease (PAD) and Diabetes Mellitus remains a critical challenge in healthcare. Current approaches lack predictive capabilities to identify high-risk patients before critical events occur. Traditional risk assessment methods rely on limited clinical parameters and often fail to capture the complex interactions between multiple risk factors. The absence of comprehensive predictive tools results in reactive rather than proactive care, leading to poor patient outcomes and increased healthcare costs. Healthcare providers need better tools to identify at-risk patients and implement preventive interventions.
Technical Overview: Bioengineering Research Professor Saeed Amal at Northeastern university has developed a digital health system integrating three key components to prevent limb amputations in PAD and diabetes patients. The system combines artificial intelligence algorithms with comprehensive patient data analysis to predict amputation risk with high accuracy. The platform integrates clinical data, imaging results, and patient-reported outcomes to generate personalized risk assessments and treatment recommendations. The AI-driven approach continuously learns from patient outcomes to improve prediction accuracy and treatment effectiveness.
Benefits:
- Achieves high prediction accuracy for amputation risk assessment
- Provides personalized treatment recommendations
- Enables proactive rather than reactive patient care
- Reduces healthcare costs through prevention
- Improves patient outcomes and quality of life
Application:
- Cardiovascular Departments: Implementation in clinics and hospitals for patient monitoring
- Primary Care Settings: Early identification of high-risk patients
- Diabetes Management Programs: Integrated care for diabetic patients
- Preventive Medicine: Population health management for at-risk groups
Opportunity:
- License
- Research collaboration
