Early Arthritis Detection Using Convolutional Neural Networks for Enhanced Diagnostic Accuracy

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Orthopedics

Background:

• Arthritis significantly impacts global quality of life, necessitating early detection for effective intervention.
• Traditional radiographic diagnosis of arthritis relies on expert interpretation, which can introduce variability and delays.
• Automated, precise, and timely identification of arthritis is crucial for improved patient outcomes.

Purpose of the Study:

• To develop an automated deep learning mechanism for precise and timely arthritis identification using knee X-ray images.
• To overcome the limitations of subjective expert interpretation in conventional arthritis diagnosis.
• To enhance early clinical intervention and patient care through accurate diagnostic support.

Main Methods:

• A Convolutional Neural Network (CNN) framework was designed for automated arthritis detection from knee X-ray images.
• The model was trained and validated on a Kaggle dataset comprising 4414 knee radiographs.
• Performance was evaluated using accuracy, precision, recall, confusion matrix, and ROC analysis, with Grad-CAM for interpretability.

Main Results:

• The proposed CNN model achieved an overall classification accuracy of 95%.
• The system demonstrated high precision (0.98) and recall (0.93) in differentiating arthritic from normal knee conditions.
• Grad-CAM visualizations successfully highlighted key radiographic features used by the model for prediction, enhancing interpretability.

Conclusions:

• Deep learning analysis of knee radiographs provides a reliable and efficient method for arthritis detection.
• Interpretability tools like Grad-CAM increase trust and transparency, making the model more applicable to clinical settings.
• The CNN-based system supports clinicians in musculoskeletal imaging, aiding early screening and patient management.