PROSPECTS OF ARTIFICIAL INTELLIGENCE IN THE DIAGNOSIS OF TEMPOROMANDIBULAR JOINT DISORDERS (TMJD)

Abstract

Temporomandibular joint disorders (TMJD) represent a group of complex musculoskeletal and neuromuscular conditions affecting the temporomandibular joint (TMJ), masticatory muscles, and associated structures. Accurate diagnosis of TMJD remains challenging due to its multifactorial etiology, variable clinical presentation, and limitations of conventional diagnostic approaches. In recent years, artificial intelligence (AI) has emerged as a promising tool in medical diagnostics, including dentistry. This paper aims to explore the prospects, current applications, and future directions of AI in diagnosing TMJD. The study reviews machine learning (ML), deep learning (DL), and computer vision techniques applied to imaging modalities such as MRI, CBCT, and clinical data analysis. AI-based systems demonstrate high accuracy in detecting structural abnormalities, classifying TMJ disorders, and predicting disease progression. However, challenges such as data quality, ethical concerns, and integration into clinical workflows remain significant. The implementation of AI in TMJD diagnostics has the potential to enhance early detection, improve diagnostic accuracy, and support personalized treatment planning.