ArticleKorean journal of orthodontics2026
Development of Leading Enhancement Assistive Planning: A three-dimensional malocclusion classification system for orthodontic treatment with clear aligners.
Article in Korean journal of orthodontics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 study used 841 anonymized 3D intraoral scans in standard tessellation language (STL) format, annotated by expert orthodontists, comprising 125 unique multilabel combinations across 4 main classes and 11 binary subclasses. The preprocessing pipeline converted STL meshes into standardized voxel grids, which served as inputs to a 3D convolutional neural network based on a modified EfficientNet architecture. A hierarchical classification architecture enabled simultaneous prediction of main-class and subclass 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 across 15 orthodontic labels treated as independent binary classification tasks, with a macro-averaged accuracy of 87.6%, precision of 86.6%, recall of 87.5%, and an F1-score of 87.1%. Hierarchical classification yielded clinically interpretable predictions for complex, overlapping malocclusions. The voxel-based pipeline supported resolutions of up to 256 × 256 × 256 with graphics processing unit acceleration for efficient inference. Integration into orthodontic CAD platforms was successfully demonstrated, enabling automated malocclusion classification at the point of care. Conclusions: LEAP is an accurate, efficient, and scalable artificial intelligence-assisted system for classifying malocclusions from 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.
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