DEVELOPMENT OF A CONVOLUTIONAL NEURAL NETWORK TO ASSESS THE SEVERITY OF KNEE OSTEOARTHRITIS

Abstract

method In this paper, we propose a novel method for the automated assessment of knee osteoarthritis severity, utilizing advanced machine learning techniques, specifically a deep neural network. Osteoarthritis is one of the most prevalent degenerative joint diseases, and its timely diagnosis is crucial for ensuring effective treatment. Traditional methods for visually assessing X-ray images of the knee joint present several limitations, including subjectivity and reliance on the experience of the clinician. Therefore, the development of automated medical image analysis techniques has become increasingly relevant. Osteoarthritis of the knee joint is one of the most common and severe degenerative diseases leading to a significant decrease in the quality of life of patients. Traditional methods of diagnosing osteoarthritis, such as visual assessment of X-ray images, depend on the subjective opinion of a specialist and his experience, which can lead to variations in the accuracy of diagnosis and timely detection of pathology. Therefore, the development and implementation of methods for automated analysis of medical images is highly relevant and has potential clinical value. In this study, we designed and trained a specialized neural network based on the ResNet-34 architecture, which has demonstrated significant effectiveness in solving computer vision problems. The network was modified to incorporate two parallel branches, each contain ing a spiral linear structure and four hidden layers. This design enables more precise identification of the knee joint area. Additionally, the architecture facilitates optimization of the loss function to account for varying pathological characteristics, such as different degrees of joint degradation, and to address the issue of class imbalance—a common challenge in medical imaging datasets. To further enhance model performance, the neural network was trained on two distinct datasets stratified by gender (male and female). This approach improved overall image quality and reduced the impact of noise introduced by artifacts during radiographic imaging. Moreover, we employed the ImagePixelSpacing technique during data preparation to standardize image resolution at 256 × 256 pixels, allowing for more accurate processing of fine details and structures within the knee joint. The network training employed state-of-the-art optimization techniques, resulting in a high level of classification accuracy. To evaluate the effectiveness of the proposed model, the Kappa test was utilized, confirming the reliability of baseline determinations. The model achieved an average accuracy of 93.76%, as demonstrated by the multiclass T-test, indicating its strong potential for clinical application. Additionally, the model’s area under the curve (AUC) score was 0.97, surpassing the results reported in previous studies in this domain. In conclusion, this research contributes significantly to the field of medical informatics and computer-based medical image analysis by offering an innovative solution for the automated assessment of osteoarthritis. This method has the potential to profoundly improve diagnostic accuracy and treatment outcomes in clinical settings. In addition, these results demonstrate the potential of the model as a reliable tool for automated assessment of the degree of osteoarthritis, which can not only improve the accuracy of diagnosis, but also facilitate the work of medical specialists. Further research may include adapting the model to analyze other joints and integrating additional functionality, such as predicting disease progression based on sequential scans.