# Computational Tools for Predicting and Understanding Knee Pain Spatial Patterns

> **NIH NIH R21** · UNIVERSITY OF ARIZONA · 2024 · $348,680

## Abstract

Project Abstract
Osteoarthritis (OA) is the most common musculoskeletal disorder, presenting a large societal burden and
affecting over 300 million worldwide. Knee pain in patients with knee OA is a leading contributor to physical
disability and a major reason for hospital visits. Prior studies that have examined the determinants of knee
pain have not considered the complexity of knee pain reporting systematically (e.g., localized, regional or
diffuse knee pain). Moreover, the existing approaches focus on accurate prediction of knee pain and have
limitation on the interpretability. There is, therefore, a critical need to address this crucial void by developing
the computational tools with accurate prediction and interpretable properties.
The overall goal of this research is to is to build computational tools to accurately predict different incident
frequent knee pain spatial patterns and interpret the association between these knee pain patterns and the
structural abnormalities (i.e., Bone Marrow Lesions (BML)) detected on Magnetic Resonance Imaging (MRI).
The central hypothesis is that early in the disease course, abnormalities of structural features on knee MRI
will be determinants of specific knee pain spatial patterns.
This study is innovative in that it focuses on incident knee pain, examining knee pain spatial patterns early in
the disease course, and taking into account different definitions of knee pain. This study is also innovative in
that the study team will leverage excellent prediction performance of deep learning and interpretable
properties of statistical approaches to predict and understand different knee pain spatial patterns.
The specific aims are 1) To adapt a Transformer-based deep learning approach for predicting the incident
localized, regional, or diffuse knee pain from MRIs in the Osteoarthritis Initiative (OAI), and 2) to develop a
novel interpretable statistical framework for characterizing the relationship between BMLs on MRIs and
incident localized, regional, or diffuse knee pain.

## Key facts

- **NIH application ID:** 10984178
- **Project number:** 1R21AR083495-01A1
- **Recipient organization:** UNIVERSITY OF ARIZONA
- **Principal Investigator:** Yong Ge
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $348,680
- **Award type:** 1
- **Project period:** 2024-09-26 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10984178

## Citation

> US National Institutes of Health, RePORTER application 10984178, Computational Tools for Predicting and Understanding Knee Pain Spatial Patterns (1R21AR083495-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10984178. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*