AI-Assisted Design of 3D-Printed Lingual Indirect Bonding Trays: A Comparative Evaluation of Bracket Transfer Accuracy

Objectives: This study investigated the use of artificial intelligence (AI) in the design of lingual bracket indirect bonding trays and its association with bracket transfer accuracy using three-dimensional (3D) printing. Methods: Digital impressions of patient’s dental arches were captured using an intraoral scanner, and orthodontic setups were virtually constructed. Brackets were virtually positioned in their ideal locations using the digital setups guided by virtual archwire templates. Indirect bonding trays were automatically generated using the AI-powered Auto Creation function of the Medit Splints application, which analyzes anatomical features to streamline design. Bracket transfer accuracy was evaluated in vivo by comparing planned and actual bracket positions across grouped and individual tray configurations. Linear and angular deviations were measured using conventional 3D inspection software. Results: Most bracket transfer errors were within clinically acceptable thresholds, although torque accuracy remained suboptimal. Grouped trays generally exhibited greater precision than individual trays in several dimensions. Conclusions: These findings support the application of AI-assisted design tools to enhance digital workflows and improve consistency in appliance fabrication.