Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning

Area of Science:

• Biomedical Engineering
• Artificial Intelligence in Healthcare
• Musculoskeletal Research

Background:

• Osteoarthritis (OA), particularly knee OA (KOA), is a leading cause of disability and a significant economic burden.
• Accurate prediction of KOA progression is vital for patient outcomes, resource allocation, and therapeutic development.
• Current KOA monitoring methods are static and focus on individual joints, limiting predictive accuracy and utility.

Purpose of the Study:

• To develop a novel method for dynamic patient monitoring in KOA, moving beyond static, single-joint assessments.
• To optimize data collection for improved KOA progression prediction and enhanced downstream applications, such as clinical trials.
• To leverage Reinforcement Learning (RL) for intelligent, cost-effective monitoring of KOA.

Main Methods:

• Proposed a novel Active Sensing (AS) approach powered by Reinforcement Learning (RL) for dynamic KOA patient monitoring.
• Developed an RL agent trained to maximize informative data collection while minimizing costs, optimizing for downstream tasks.
• Utilized a custom reward function for multi-part disease progression monitoring and employed multimodal deep learning.

Main Results:

• The proposed RL-based method demonstrated superior performance compared to existing state-of-the-art models in predicting KOA progression.
• The approach enables dynamic, multi-joint monitoring, leading to more informative data collection.
• The system requires no human input during testing, enhancing efficiency.

Conclusions:

• The novel RL-powered AS approach represents a significant advancement in KOA monitoring and prediction.
• This method offers a more dynamic, efficient, and accurate way to track KOA progression across multiple body parts.
• The findings pave the way for next-generation KOA clinical trials and improved patient management strategies.