OsteoPredict: An AI-Driven Application for Osteoarthritis Risk Assessment, Progression Monitoring, and Treatment Optimization
Session Number
MEDH 39
Advisor(s)
Anne-Marie Malfait and Rachel Miller; Rush University
Discipline
Medical and Health Sciences
Start Date
17-4-2025 2:45 PM
End Date
17-4-2025 3:00 PM
Abstract
Osteoarthritis (OA) is a lead cause of chronic pain and disability, yet early detection and personalized treatment remain challenging. OsteoPredict is an AI-driven application designed to improve OA management by integrating predictive risk modeling, patient monitoring, and evidence-based treatment recommendations.
The application employs a machine learning-driven risk stratification model utilizing demographic, clinical, and lifestyle factors to predict an individual’s likelihood of OA onset and progression. Through a structured patient survey and physician-inputted data, OsteoPredict categorizes users into different risk levels and provides preventative care recommendations. Additionally, the platform incorporates advanced imaging analysis to detect early structural changes indicative of OA helping clinicians in objective diagnostics.
For healthcare professionals, OsteoPredict serves as a decision-support tool linking patients to officially approved treatment protocols from organizations such as the FDA, ACR, and AAOS. Patients can access a curated database of conservative therapies, pharmacologic interventions, and emerging clinical trials that are aligned with current medical standards. Furthermore, the app features progress tracking capabilities, allowing patients to log symptoms, and treatment efficacy over time.
OsteoPredict integrates AI, musculoskeletal research, and telemedicine to enhance early intervention and optimize osteoarthritis treatment. This study details its development and potential impact on patient care and clinical decision-making.
OsteoPredict: An AI-Driven Application for Osteoarthritis Risk Assessment, Progression Monitoring, and Treatment Optimization
Osteoarthritis (OA) is a lead cause of chronic pain and disability, yet early detection and personalized treatment remain challenging. OsteoPredict is an AI-driven application designed to improve OA management by integrating predictive risk modeling, patient monitoring, and evidence-based treatment recommendations.
The application employs a machine learning-driven risk stratification model utilizing demographic, clinical, and lifestyle factors to predict an individual’s likelihood of OA onset and progression. Through a structured patient survey and physician-inputted data, OsteoPredict categorizes users into different risk levels and provides preventative care recommendations. Additionally, the platform incorporates advanced imaging analysis to detect early structural changes indicative of OA helping clinicians in objective diagnostics.
For healthcare professionals, OsteoPredict serves as a decision-support tool linking patients to officially approved treatment protocols from organizations such as the FDA, ACR, and AAOS. Patients can access a curated database of conservative therapies, pharmacologic interventions, and emerging clinical trials that are aligned with current medical standards. Furthermore, the app features progress tracking capabilities, allowing patients to log symptoms, and treatment efficacy over time.
OsteoPredict integrates AI, musculoskeletal research, and telemedicine to enhance early intervention and optimize osteoarthritis treatment. This study details its development and potential impact on patient care and clinical decision-making.