International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Context-Aware Clinical Decision Support System for Standardized Diagnosis of Temporomandibular Disorders |
|---|---|
| ABSTRACT | Temporomandibular Joint Disorders (TMD) are common jaw-related conditions that cause pain, joint dysfunction, and difficulty in mouth movement. Accurate diagnosis of TMD is challenging due to overlapping symptoms and reliance on subjective clinical judgment. This project proposes an MCP-based Clinical Decision Support System (CDSS) to assist clinicians in diagnosing TMD more accurately and consistently. The system employs the Model Context Protocol (MCP) to securely manage and maintain contextual clinical data during the diagnostic process. Multiple diagnostic parameters, such as patient symptoms, clinical examination findings, medical history, and risk factors, are analyzed using a multi-criteria decision-making approach. Each parameter is assigned an appropriate weight based on clinical importance. A rule-based decision engine processes the data and classifies patients into probable TMD categories. The system also provides confidence scores and suggested clinical actions for further evaluation or treatment. A user-friendly interface allows easy data entry and real-time result visualization. The proposed solution reduces diagnostic subjectivity and enhances the standardization of clinical assessments. Testing with case-based datasets demonstrates improved consistency and faster diagnosis compared to manual methods. The system supports early detection and better treatment planning. It is suitable for use in dental clinics and primary healthcare centers. Future enhancements may include integration with medical imaging and machine learning models. Overall, the MCP-based CDSS improves diagnostic efficiency and patient care in TMD management. |
| AUTHOR | M. VANITHA, DR. M. SENTHILKUMAR, ARJUN C, GOKUL S, SURYA PRAKASH S Assistant Professor, Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, India Professor, Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, India Department of Artificial Intelligence and Data Science, Kongunadu College of Engineering and Technology, Trichy, India |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403018 |
| pdf/18_A Context-Aware Clinical Decision Support System for Standardized Diagnosis of Temporomandibular Disorders.pdf | |
| KEYWORDS | |
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