# Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1609.02469 · HTML: https://mlsanity.com/paper/1609.02469/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2016.

## Built on by (most cited first, 17 of 17)

- [Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network](https://mlsanity.com/paper/2106.14292/) (2021)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Give me a knee radiograph, I will tell you where the knee joint area is: a deep convolutional neural network adventure](https://mlsanity.com/paper/2202.05382/) (2022)
- [Confidence-Driven Deep Learning Framework for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2303.13203/) (2023)
- [Automatic diagnosis of knee osteoarthritis severity using Swin transformer](https://mlsanity.com/paper/2307.04442/) (2023)
- [A Neural Template Matching Method to Detect Knee Joint Areas](https://mlsanity.com/paper/2209.11791/) (2022)
- [A generalized framework to predict continuous scores from medical ordinal labels](https://mlsanity.com/paper/2305.19097/) (2023)
- [Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment](https://mlsanity.com/paper/2403.09947/) (2024)
- [MeniMV: A Multi-view Benchmark for Meniscus Injury Severity Grading](https://mlsanity.com/paper/2512.18437/) (2025)
- [An overview of deep learning in medical imaging](https://mlsanity.com/paper/2202.08546/) (2022)
- [Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review](https://mlsanity.com/paper/2308.09380/) (2023)
- [Improving Image Classification of Knee Radiographs: An Automated Image Labeling Approach](https://mlsanity.com/paper/2309.02681/) (2023)
- [Embedded Deployment of Semantic Segmentation in Medicine through Low-Resolution Inputs](https://mlsanity.com/paper/2403.05340/) (2024)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)
- [Diffusion-based Counterfactual Augmentation: Towards Robust and Interpretable Knee Osteoarthritis Grading](https://mlsanity.com/paper/2506.15748/) (2025)
- [Morphology-Aware KOA Classification: Integrating Graph Priors with Vision Models](https://mlsanity.com/paper/2510.21801/) (2025)
- [ClinNet: Evidential Ordinal Regression with Bilateral Asymmetry and Prototype Memory for Knee Osteoarthritis Grading](https://mlsanity.com/paper/2601.17315/) (2026)

## Abstract (arXiv)

This paper proposes a new approach to automatically quantify the severity of knee osteoarthritis (OA) from radiographs using deep convolutional neural networks (CNN). Clinically, knee OA severity is assessed using Kellgren & Lawrence (KL) grades, a five point scale. Previous work on automatically predicting KL grades from radiograph images were based on training shallow classifiers using a variety of hand engineered features. We demonstrate that classification accuracy can be significantly improved using deep convolutional neural network models pre-trained on ImageNet and fine-tuned on knee OA images. Furthermore, we argue that it is more appropriate to assess the accuracy of automatic knee OA severity predictions using a continuous distance-based evaluation metric like mean squared error than it is to use classification accuracy. This leads to the formulation of the prediction of KL grades as a regression problem and further improves accuracy. Results on a dataset of X-ray images and KL grades from the Osteoarthritis Initiative (OAI) show a sizable improvement over the current state-of-the-art.