Artificial Intelligence in Invisible Orthodontics: From Digital Impressions to Adaptive Treatment Management

Abstract

Digital transformation in orthodontics has significantly revolutionized clinical practices over recent decades. Advancements such as intraoral scanners, 3D imaging (including cone-beam computed tomography, CBCT), and advanced treatment planning software have increased diagnostic accuracy and the ability to design personalized treatments. These technologies have primarily focused on static aspects and still require continuous human oversight. Machine learning algorithms, particularly convolutional neural networks (CNNs) and artificial neural networks (ANNs), offer superior performance in tasks such as identifying anatomical landmarks on radiographic images, assessing skeletal maturity, and predicting treatment outcomes. Research has shown that AI in static diagnosis and planning has achieved an accuracy greater than 90%, outperforming traditional methods. Moreover, AI reduces processing time, minimizes operator performance variability, and serves as a supportive tool in digital orthodontic workflows. Despite these advancements, there remains a significant gap in research regarding adaptive AI for treatments involving clear aligners. Most studies focus on static planning, while dynamic aspects, such as real-time treatment adjustments based on the patient's biological responses or deviations in tooth movement, are less explored. This research gap may arise from factors such as patient biological diversity, a lack of real clinical data, and the need for multi-center validation. Adaptive AI, based on reinforcement learning and digital twin modeling, has the potential to reduce the average treatment duration by approximately 3 to 4 months and increase patient satisfaction. The aim of this review is to provide a comprehensive examination of AI's role in real-time management of invisible orthodontic treatments, focusing on clear aligner-based treatments. This study analyzes AI applications going from digital data processing to integration with supervisory platforms, discussing challenges, limitations, and future perspectives. This review not only summarizes current achievements but also proposes practical solutions to overcome existing barriers and presents adaptive AI as a pivotal tool for the future of orthodontics.