Radiographic Evaluation of Implant Stability and Osseointegration in Adult Orthodontic Patients

Radiographic evaluation is essential for assessing implant stability and osseointegration in adult orthodontic patients. The success of temporary anchorage devices (TADs) and mini-implants depends on primary stability, achieved through mechanical engagement, and secondary stability, influenced by bone remodeling. While traditional clinical methods, such as mobility testing, provide subjective assessments, radiographic imaging offers objective insights into bone-implant interactions. Periapical and panoramic radiographs are commonly used but are limited by their two-dimensional (2D) nature. Cone beam computed tomography (CBCT) has emerged as the gold standard, providing three-dimensional (3D) visualization of cortical bone thickness, marginal bone loss, and peri-implant adaptations. However, challenges such as image artifacts, radiation exposure, and observer variability persist. Implant stability is influenced by factors like bone density, cortical thickness, insertion torque, and patient-specific variables, including systemic conditions, genetic predisposition, and lifestyle habits. Emerging techniques such as resonance frequency analysis (RFA) complement radiographic findings by providing quantitative stability assessments. Additionally, artificial intelligence (AI)-driven radiographic analysis is improving diagnostic accuracy, automating bone density evaluation, and predicting implant success. Future advancements in low-dose CBCT protocols, AI-assisted diagnostics, and digital treatment planning aim to optimize implant placement and long-term stability assessment. By integrating multimodal imaging approaches with biomechanical and AI-driven predictive modeling, clinicians can enhance treatment planning, reduce implant failure rates, and improve orthodontic outcomes. This review underscores the importance of advanced imaging techniques in implant stability assessment and highlights the need for continued research in AI-driven diagnostics and minimally invasive evaluation methods.