Enhanced Rheumatoid Arthritis Treatment Optimization Through Deep Learning

• Published in: 2025 3rd International Conference on Disruptive Technologies (ICDT) pp. 1343 - 1348
• Main Authors: Arcot, Siva Venkatesh, Buvaneswari, T., Kavitha, K.R., Manickavasagam, R., M, Suguna, Muthulekshmi, M
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.03.2025
• Subjects: Accuracy; Adaptation models; Arthritis; Biomedical imaging; Convolutional Neural Network; Convolutional neural networks; Data models; Deep learning; Imaging Analysis; Optimization; Personalized Medicine; Predictive models; Rheumatoid Arthritis; Soft sensors; Treatment Optimization
• DOI: 10.1109/ICDT63985.2025.10986740

Inflammation and joint destruction are hallmarks of Rheumatoid Arthritis (RA), a chronic autoimmune condition. To improve patient outcomes, it is essential to optimize treatment plans. According to this research, integrating deep learning (DL) methods into a Convolutional Neural Network (CNN) provides a new method to optimize RA treatments. The CNN model is used to decipher intricate medical data, such as imaging and patient records, find trends, and predict how patients react to treatment. To assess the efficacy of the proposed model in predicting ideal treatment methods, it was trained on a large dataset that included patient images and clinical data. The CNN model offers a potential tool for doctors to adapt RA management plans since results show it significantly improves prediction accuracy and treatment personalization. This method allows for data-driven decisions in RA management, improving patient care and enhancing treatment performance.