Early Detection of Knee Arthritis Using AI: Enhancing Diagnostic Accuracy with Deep Learning

This research explores the use of deep learning models, specifically convolutional neural networks like EfficientNetB0, DenseNet121, and InceptionV3, to improve the accuracy, consistency, and accessibility of knee arthritis diagnosis through automated classification of disease severity, while emphasizing the need for explainable AI to enhance clinical trust and adoption.

Arthritis, particularly of the knee, is one of the most common chronic diseases worldwide and seriously hinders patients' mobility and quality of life. Early diagnosis is essential for effective intervention; however, diagnosis using X-ray imaging is difficult due to the subtlety and variability manifested in the various states of the disease. Deep learning methods, in particular, convolutional neural networks (CNNs), are proposed for the automatic grading of knee arthritis into five classes: Normal, Doubtful, Mild, Moderate, and Severe. This is an artificial intelligence system that avoids the many difficulties of manual diagnosis, which are inconsistent between radiologists and very time-consuming with current methods, to increase the diagnostic accuracy and consistency, especially in developing or resource-constrained settings with limited specialist access. The study uses transfer learning with state-of-the-art models, such as EfficientNetB0, DenseNet121, and InceptionV3, to improve the classification of arthritis severity on X-ray images. Pre-trained layers on ImageNet datasets were fine-tuned for the knee arthritis dataset by adding custom classification heads to each model. Data preprocessing methods, including resizing and normalization, were also applied to ensure uniformity throughout the dataset. Their performances were tested with the use of such metrics as accuracy, precision, recall, and F1-score, validated against different stages in the development of arthritis. Of special note was EfficientNetB0, showing better results of up to an 85% accuracy score for this overall scenario. While the models showed promising results, they still struggled with the accurate classification of cases of Mild arthritis, indicating a need for further refinement. This study has brought out the potential of AI-based diagnostic tools in reducing diagnostic variability, speeding up the determination of arthritis severity, and improving patient outcomes. However, it also responds to key concerns about the "black box" nature of deep learning models, which can impede clinical adoption. To tackle this challenge, the research highlights the importance of explainable AI methodologies in building trust in practitioners of healthcare through the interpretability of predictive models. This study has significant implications for the medical field, as it demonstrates the potential of AI in helping radiologists and primary care providers achieve better accuracy and consistency in diagnosing knee arthritis. Their deployment in mobile or cloud-based settings may greatly increase the access to diagnostic tools in remote or disadvantaged areas. Future efforts in research will go in the direction of increasing diversity in datasets, adding multimodal data, and interpretability of models to encourage widespread adoption in clinical practice. To make effective and scalable artificial intelligence solutions available for more precise, rapid, and fairer diagnoses of knee arthritis, with an overall goal to have better patient management and care in mind.

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