AI-Powered 3D Orthodontic Diagnosis with LEAP

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Artificial intelligence is rapidly transforming modern orthodontics, especially in the area of Digital Treatment Planning, Clear Aligner Workflows, and AI Orthodontics. One of the latest developments in this field is the LEAP System – a 3D AI-based Malocclusion Classification platform designed to automate orthodontic diagnosis using Intraoral Scan Data.

Traditionally, diagnosing malocclusions requires detailed analysis by experienced orthodontists using photographs, radiographs, and dental model evaluations. This process can be time-consuming and may vary depending on clinical experience. LEAP was developed to improve diagnostic consistency by combining Deep Learning with Three-Dimensional Dental Scans for automated classification.

The system works by converting STL Intraoral Scan Files into voxelized 3D Models before processing them through a customized EfficientNet-based 3D Convolutional Neural Network (CNN). LEAP can identify multiple orthodontic conditions including Class I, Class II Division 1, Class II Division 2, Class III, Deep Bite, Open Bite, Crossbite, Scissor Bite, Spacing, and Crowding.

Researchers used a dataset of 841 Expert-Labeled 3D Dental Models to train and validate the system. According to the study, LEAP achieved an overall classification accuracy of 90.8%, with especially strong performance in Anterior Open Bite and Posterior Open Bite cases.

One of LEAP’s biggest advantages is its ability to provide Real-Time Automated Diagnostic Feedback directly within Orthodontic CAD Software. This can help clinicians reduce manual workload, improve workflow efficiency, and support more standardized Clear Aligner Treatment Planning.

The researchers also highlighted the future potential of integrating LEAP into AI-Assisted Orthodontic Treatment Planning Systems, Intraoral Scanners, and Digital CAD Platforms. As Digital Dentistry continues to evolve, AI systems like LEAP may become an important part of faster, smarter, and more precise orthodontic workflows.

Sources:
https://doi.org/10.4041/kjod25.214
https://pubmed.ncbi.nlm.nih.gov/37276298/
https://doi.org/10.1186/s13005-024-00448-8
https://doi.org/10.1038/s41598-023-32514-7
https://doi.org/10.3389/fdmed.2023.1085251

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