# Automatic Grading of Individual Knee Osteoarthritis Features in Plain Radiographs using Deep Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1907.08020 · HTML: https://mlsanity.com/paper/1907.08020/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 18 of 18)

- [Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data](https://mlsanity.com/paper/2210.13889/) (2022)
- [Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations](https://mlsanity.com/paper/2203.01933/) (2022)
- [Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis](https://mlsanity.com/paper/2311.06118/) (2023)
- [Automatic diagnosis of knee osteoarthritis severity using Swin transformer](https://mlsanity.com/paper/2307.04442/) (2023)
- [Unsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification](https://mlsanity.com/paper/2212.07023/) (2022)
- [Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks](https://mlsanity.com/paper/2311.05798/) (2023)
- [Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification](https://mlsanity.com/paper/2311.05799/) (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)
- [A Progressive Risk Formulation for Enhanced Deep Learning based Total Knee Replacement Prediction in Knee Osteoarthritis](https://mlsanity.com/paper/2406.10119/) (2024)
- [Knee arthritis severity measurement using deep learning: a publicly available algorithm with a multi-institutional validation showing radiologist-level performance](https://mlsanity.com/paper/2203.08914/) (2022)
- [Identity-Consistent Diffusion Network for Grading Knee Osteoarthritis Progression in Radiographic Imaging](https://mlsanity.com/paper/2407.21381/) (2024)
- [Toward Cost-efficient Adaptive Clinical Trials in Knee Osteoarthritis with Reinforcement Learning](https://mlsanity.com/paper/2408.02349/) (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)
- [Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading](https://mlsanity.com/paper/2511.22143/) (2025)
- [PSSF: Early osteoarthritis detection using physical synthetic knee X-ray scans and AI radiomics models](https://mlsanity.com/paper/2601.11642/) (2026)
- [Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis](https://mlsanity.com/paper/2604.23435/) (2026)
- [Knee Osteoarthritis Severity Grading Using Optimized Deep Learning and LLM-Driven Intelligent AI on Computationally Limited Systems](https://mlsanity.com/paper/2605.05731/) (2026)

## Abstract (arXiv)

Knee osteoarthritis (OA) is the most common musculoskeletal disease in the world. In primary healthcare, knee OA is diagnosed using clinical examination and radiographic assessment. Osteoarthritis Research Society International (OARSI) atlas of OA radiographic features allows to perform independent assessment of knee osteophytes, joint space narrowing and other knee features. This provides a fine-grained OA severity assessment of the knee, compared to the gold standard and most commonly used Kellgren-Lawrence (KL) composite score. However, both OARSI and KL grading systems suffer from moderate inter-rater agreement, and therefore, the use of computer-aided methods could help to improve the reliability of the process. In this study, we developed a robust, automatic method to simultaneously predict KL and OARSI grades in knee radiographs. Our method is based on Deep Learning and leverages an ensemble of deep residual networks with 50 layers, squeeze-excitation and ResNeXt blocks. Here, we used transfer learning from ImageNet with a fine-tuning on the whole Osteoarthritis Initiative (OAI) dataset. An independent testing of our model was performed on the whole Multicenter Osteoarthritis Study (MOST) dataset. Our multi-task method yielded Cohen's kappa coefficients of 0.82 for KL-grade and 0.79, 0.84, 0.94, 0.83, 0.84, 0.90 for femoral osteophytes, tibial osteophytes and joint space narrowing for lateral and medial compartments respectively. Furthermore, our method yielded area under the ROC curve of 0.98 and average precision of 0.98 for detecting the presence of radiographic OA (KL ≥ 2), which is better than the current state-of-the-art.