Development of Leading Enhancement Assistive Planning: A three-dimensional tooth classification system for orthodontic treatment with clear aligners

Objective: To develop and validate the Leading Enhancement Assistive Planning (LEAP) system, a deep learning-based tool for automated malocclusion classification from three-dimensional (3D) intraoral scans, integrated into orthodontic computer-aided design (CAD) workflows to support clinical diagnosis and treatment planning. Methods: This retrospective study utilized 841 anonymized 3D intraoral scans in the standard tessellation language (STL) format, representing 8 malocclusion categories, labeled and validated by expert orthodontists. Preprocessing converted the STL meshes into standardized voxel grids, which served as inputs to a modified EfficientNet-based 3D convolutional neural network. The hierarchical classification logic distinguished primary and secondary malocclusions. Model performance was assessed using accuracy, precision, recall, and F1-score metrics on a held-out test set. Integration into orthodontic CAD software was demonstrated to provide real-time diagnostic feedback. Results: The LEAP system achieved robust classification performance, with 90.8% accuracy, 87.5% precision, 91.3% recall, and an F1 score of 89.2% across the eight malocclusion classes. Hierarchical logic enabled a clinically interpretable output for complex cases with overlapping conditions. The voxel-based pipeline supported resolutions of up to 256 × 256 × 256 with graphics processing unit acceleration for efficient inference. Readiness for integration into CAD platforms was demonstrated, providing automated malocclusion classification at the point of care. Conclusions: LEAP is an accurate, efficient, and scalable artificial intelligence-assisted system for classifying malocclusions using 3D dental scans. Its integration into orthodontic CAD software offers standardized, real-time diagnostic support, potentially improving workflow efficiency and consistency. The LEAP system may enhance diagnostic accuracy, reduce variability, and serve as a valuable decision-support tool in orthodontic practice.