AI-Generated Customized Aligner Movement Based On Individual Bone Remodeling Biomarkers.

Abstract

Artificial intelligence (AI) is transforming orthodontics by enabling highly personalized treatment strategies that account for individual biological variability rather than relying on standardized protocols. One emerging innovation is the generation of customized aligner movement based on patient-specific bone remodeling biomarkers. By integrating biomarker profiles obtained from saliva, gingival crevicular fluid, blood, and advanced imaging with clinical records and digital dental models, AI algorithms can predict the rate and pattern of bone remodeling during orthodontic tooth movement. This approach allows clinicians to optimize aligner staging, force application, and treatment intervals according to each patient's biological response. AI-driven predictive models also facilitate continuous monitoring of treatment progress, allowing dynamic adjustments that improve tooth movement accuracy while minimizing adverse effects such as root resorption, delayed movement, and treatment relapse. The combination of machine learning, multimodal imaging, and biomarker analysis supports the development of adaptive orthodontic workflows that enhance precision, efficiency, and patient-centered care. Although challenges remain regarding biomarker standardization, data integration, clinical validation, ethical considerations, and regulatory implementation, ongoing technological advances continue to strengthen the feasibility of this precision-based approach. AI-generated customized aligner movement represents a promising advancement toward biologically informed orthodontics, offering improved treatment predictability, enhanced clinical outcomes, and more individualized patient care.