# Deep Learning-based Imaging Biomarkers for Knee Osteoarthritis

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2021 · $482,108

## Abstract

ABSTRACT
In the U.S., more than 600,000 knee osteoarthritis (OA)-related total knee joint replacement (TKR) cases are
reported every year, exceeding $17 billion estimated direct costs annually. There is a growing need for disease-
modifying therapies that prevent or delay the need for TKR. However, development of such therapies remains
challenging due to the lack of objective and measurable OA biomarkers for disease progression. The course of
the OA is highly variable between individuals and the OA progresses too slowly, making it difficult to identify
sensitive OA biomarkers capable of capturing minor changes on the knee joint. This has slowed development of
effective therapies and prevents physicians from providing the most effective advice about minimizing the need
for TKR. In this project, our goal is to develop imaging biomarkers to monitor minor OA-related changes in knee
joint health that lead to TKR. To achieve this goal, we will combine novel deep learning algorithms with clinical
and imaging data from the Osteoarthritis Initiative (OAI). The OAI dataset includes clinical data, biospecimens,
radiographs, and magnetic resonance (MR) images collected over 8 years. The proposed project has three
Specific Aims: (i) to develop an automated OA-relevant biomarker identification tool from the bilateral
posteroanterior fixed-flexion knee radiographs using deep convolutional neural networks (CNNs) and recurrent
neural networks (RNNs) combined with the OA progression outcome of subjects (n = 882); (ii) to develop an
automated OA-relevant biomarker identification tool from structural and compositional MR images using 3D
CNNs with RNNs combined with the OA progression outcome of subjects (n = 882); and (iii) to determine whether
deep learning–based imaging biomarkers can act as surrogates to predict the OA progression using a subject
cohort (n = 296) independent of the cohort used to identify imaging biomarkers. The proposed project will couple
deep learning with diagnostic radiology to unveil key combinations of OA-relevant features directly from images
with minimal user interaction. This will facilitate fast individualized assessment of OA progression using whole
knee joint images directly. If successful, this study will bring new insights into the development of imaging
biomarkers for OA progression and more broadly into our understanding and treatment of OA. The knowledge
gained in this project will help to advance close monitoring of OA progression by opening new perspectives on
the regions and parameters for potential inclusion in both intervention studies and clinical practice.

## Key facts

- **NIH application ID:** 10137892
- **Project number:** 5R01AR074453-03
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** Cem Murat Deniz
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $482,108
- **Award type:** 5
- **Project period:** 2019-07-02 → 2024-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10137892

## Citation

> US National Institutes of Health, RePORTER application 10137892, Deep Learning-based Imaging Biomarkers for Knee Osteoarthritis (5R01AR074453-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10137892. Licensed CC0.

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