# Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach

arXiv: https://arxiv.org/abs/1710.10589 · HTML: https://mlsanity.com/paper/1710.10589/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2017.

## Built on by (most cited first, 30 of 42)

- [Fr'echet Radiomic Distance (FRD): A Versatile Metric for Comparing Medical Imaging Datasets](https://mlsanity.com/paper/2412.01496/) (2024)
- [The Effect of Intrinsic Dataset Properties on Generalization: Unraveling Learning Differences Between Natural and Medical Images](https://mlsanity.com/paper/2401.08865/) (2024)
- [Improving the repeatability of deep learning models with Monte Carlo dropout](https://mlsanity.com/paper/2202.07562/) (2022)
- [The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning](https://mlsanity.com/paper/2207.02797/) (2022)
- [Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations](https://mlsanity.com/paper/2203.01933/) (2022)
- [Knee Osteoarthritis Severity Prediction using an Attentive Multi-Scale Deep Convolutional Neural Network](https://mlsanity.com/paper/2106.14292/) (2021)
- [Deep learning-based algorithm for assessment of knee osteoarthritis severity in radiographs matches performance of radiologists](https://mlsanity.com/paper/2207.12521/) (2022)
- [Knee Cartilage Defect Assessment by Graph Representation and Surface Convolution](https://mlsanity.com/paper/2201.04318/) (2022)
- [Transformer with Selective Shuffled Position Embedding and Key-Patch Exchange Strategy for Early Detection of Knee Osteoarthritis](https://mlsanity.com/paper/2304.08364/) (2023)
- [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)
- [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)
- [DeepRA: Predicting Joint Damage From Radiographs Using CNN with Attention](https://mlsanity.com/paper/2102.06982/) (2021)
- [A generalized framework to predict continuous scores from medical ordinal labels](https://mlsanity.com/paper/2305.19097/) (2023)
- [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)
- [Temporal Evolution of Knee Osteoarthritis: A Diffusion-based Morphing Model for X-ray Medical Image Synthesis](https://mlsanity.com/paper/2408.00891/) (2024)
- [Pre-processing and Compression: Understanding Hidden Representation Refinement Across Imaging Domains via Intrinsic Dimension](https://mlsanity.com/paper/2408.08381/) (2024)
- [VL-OrdinalFormer: Vision Language Guided Ordinal Transformers for Interpretable Knee Osteoarthritis Grading](https://mlsanity.com/paper/2601.00879/) (2025)
- [Self-Supervised Learning for Knee Osteoarthritis: Diagnostic Limitations and Prognostic Value of Hospital Data](https://mlsanity.com/paper/2603.24903/) (2026)
- [A Practical Framework for ROI Detection in Medical Images -- a case study for hip detection in anteroposterior pelvic radiographs](https://mlsanity.com/paper/2103.01584/) (2021)
- [Training Sparse Neural Network by Constraining Synaptic Weight on Unit Lp Sphere](https://mlsanity.com/paper/2103.16013/) (2021)
- [Multipath CNN with alpha matte inference for knee tissue segmentation from MRI](https://mlsanity.com/paper/2109.14249/) (2021)
- [Coherence Learning using Keypoint-based Pooling Network for Accurately Assessing Radiographic Knee Osteoarthritis](https://mlsanity.com/paper/2112.09177/) (2021)
- [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)
- [Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis Detection](https://mlsanity.com/paper/2302.13336/) (2023)
- [Deciphering knee osteoarthritis diagnostic features with explainable artificial intelligence: A systematic review](https://mlsanity.com/paper/2308.09380/) (2023)

## Abstract (arXiv)

Knee osteoarthritis (OA) is the most common musculoskeletal disorder. OA diagnosis is currently conducted by assessing symptoms and evaluating plain radiographs, but this process suffers from subjectivity. In this study, we present a new transparent computer-aided diagnosis method based on the Deep Siamese Convolutional Neural Network to automatically score knee OA severity according to the Kellgren-Lawrence grading scale. We trained our method using the data solely from the Multicenter Osteoarthritis Study and validated it on randomly selected 3,000 subjects (5,960 knees) from Osteoarthritis Initiative dataset. Our method yielded a quadratic Kappa coefficient of 0.83 and average multiclass accuracy of 66.71% compared to the annotations given by a committee of clinical experts. Here, we also report a radiological OA diagnosis area under the ROC curve of 0.93. We also present attention maps -- given as a class probability distribution -- highlighting the radiological features affecting the network decision. This information makes the decision process transparent for the practitioner, which builds better trust toward automatic methods. We believe that our model is useful for clinical decision making and for OA research; therefore, we openly release our training codes and the data set created in this study.