Automatic Knee Osteoarthritis Diagnosis from Plain Radiographs: A Deep Learning-Based Approach (1710.10589v1)

Abstract: 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.

• The paper presents a novel deep Siamese network that automates knee OA diagnosis from radiographs, reducing subjectivity in clinical assessments.
• Methodology leverages distinct training and testing datasets from MOST and OAI, achieving a quadratic Kappa of 0.83, 66.71% accuracy, and 0.93 AUC.
• The study provides interpretable attention maps and open-access annotations/code, promoting reproducibility and future research enhancements.

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

The paper presents a novel approach for automating the diagnosis of knee osteoarthritis (OA) from plain radiographs using a Deep Siamese Convolutional Neural Network. The research addresses key limitations in the current clinical practice of OA diagnosis, primarily the subjectivity in evaluating plain radiographs for identifying disease severity using the Kellgren-Lawrence (KL) grading scale. By introducing a computer-aided diagnosis (CADx) system, the paper seeks to offer a more objective and systematic method for OA evaluation.

Methodology and Results

The authors employed a deep learning framework that leverages a Siamese network architecture to capitalize on the symmetry of knee joint images, thus effectively reducing the model complexity and enhancing its robustness against noise. The model was trained using imaging data from the Multicenter Osteoarthritis Study (MOST) and validated with a subset of 3,000 subjects from the Osteoarthritis Initiative (OAI) dataset. This methodology ensured that the training and testing datasets had distinct distributions, thereby aiming to assess the model's generalization capabilities effectively.

Numerically, the proposed model yielded a quadratic Kappa coefficient of 0.83, an average multi-class accuracy of 66.71%, and an impressive area under the ROC curve (AUC) of 0.93 for radiological OA diagnosis. These metrics reflect both the alignment of the model's predictions with human expert annotations and its success in distinguishing between different severities of OA. Notable from a clinical accuracy standpoint is the improved classification performance in discerning early OA (KL-2), a challenging diagnostic task that requires high sensitivity to subtle radiographic changes.

Implications and Future Directions

The implications of this research are substantial for both clinical practice and OA research. The CADx system proposed offers a viable solution for reducing diagnostic subjectivity, potentially increasing diagnostic certainty and reducing diagnostic time. The model's transparency is enhanced by its ability to generate attention maps, which visually display which image regions most influence the network's decisions, thus providing interpretable outputs that can assist clinicians in trust-building and validation.

One of the significant contributions of this work is the public release of the dataset annotations and training code, which can facilitate reproducibility and further research in the field. The open-access model could serve as a foundation for developing more refined tools and inviting collaborative developments that enhance diagnostic capabilities further.

From a future research perspective, while this paper validates the proposed approach across two distinct datasets, there remains an opportunity to explore its scalability and applicability across diverse clinical settings and imaging modalities. Additionally, given OA’s multi-factorial pathology, integrating multimodal data, such as clinical history and genetic information, could further enhance diagnostic precision and treatment personalization.

Conclusion

This paper makes a robust contribution towards enhancing the clinical utility of machine learning in musculoskeletal radiology. The approach not only sets a new benchmark by achieving competitive numerical outcomes but also embraces the interpretative collaboration between machine learning models and clinical experts. As AI continues to integrate into healthcare, such systems hold promise for advancing early disease detection and management strategies.