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

TL;DR

This paper introduces a reinforcement learning-based adaptive method for monitoring knee osteoarthritis progression, optimizing data collection to improve predictive accuracy and trial efficiency.

Contribution

It presents a novel RL-powered active sensing approach that dynamically monitors KOA progression across multiple joints, outperforming static models.

Findings

Outperforms existing models in predictive accuracy

Reduces costs by optimizing data collection

No human input needed during testing

Abstract

Osteoarthritis (OA) is the most common musculoskeletal disease, with knee OA (KOA) being one of the leading causes of disability and a significant economic burden. Predicting KOA progression is crucial for improving patient outcomes, optimizing healthcare resources, studying the disease, and developing new treatments. The latter application particularly requires one to understand the disease progression in order to collect the most informative data at the right time. Existing methods, however, are limited by their static nature and their focus on individual joints, leading to suboptimal predictive performance and downstream utility. Our study proposes a new method that allows to dynamically monitor patients rather than individual joints with KOA using a novel Active Sensing (AS) approach powered by Reinforcement Learning (RL). Our key idea is to directly optimize for the downstream task by training an agent that maximizes informative data collection while minimizing overall costs. Our RL-based method leverages a specially designed reward function to monitor disease progression across multiple body parts, employs multimodal deep learning, and requires no human input during testing. Extensive numerical experiments demonstrate that our approach outperforms current state-of-the-art models, paving the way for the next generation of KOA trials.