ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding

2025-11-18

AI Summary

Semantic parsing of intraoral 3D scans is hindered by scarce annotated data, device heterogeneity, geometric incompleteness, and absent texture. Method: We propose a training-free structured understanding framework that integrates a geometry-aware dental-arch flattening module to generate multi-view projections, and constructs a Dental Knowledge Base (DKB) unifying tooth-level ontologies, eruption-stage rules, and clinical semantics. Crucially, we pioneer ontology-guided visual-language modeling by embedding domain-specific ontological knowledge into a vision-language model (VLM) to enable knowledge-constrained reasoning. Results: Evaluated on 1,060 orthodontic cases, our method significantly outperforms supervised models and prompt-based VLM baselines across teeth counting, anatomical segmentation, eruption staging, and caries/edentulism identification—particularly under low-quality scans—demonstrating superior robustness and cross-device generalizability.

Technology Category

• Knowledge Representation and Reasoning: Ontologies
• Computer Vision: Language and Vision
• Data Mining & Knowledge Management: Linked Open Data, Knowledge Graphs & KB Completion

Application Category

• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

A structured understanding of intraoral 3D scans is essential for digital orthodontics. However, existing deep-learning approaches rely heavily on modality-specific training, large annotated datasets, and controlled scanning conditions, which limit generalization across devices and hinder deployment in real clinical workflows. Moreover, raw intraoral meshes exhibit substantial variation in arch pose, incomplete geometry caused by occlusion or tooth contact, and a lack of texture cues, making unified semantic interpretation highly challenging. To address these limitations, we propose ArchMap, a training-free and knowledge-guided framework for robust structured dental understanding. ArchMap first introduces a geometry-aware arch-flattening module that standardizes raw 3D meshes into spatially aligned, continuity-preserving multi-view projections. We then construct a Dental Knowledge Base (DKB) encoding hierarchical tooth ontology, dentition-stage policies, and clinical semantics to constrain the symbolic reasoning space. We validate ArchMap on 1060 pre-/post-orthodontic cases, demonstrating robust performance in tooth counting, anatomical partitioning, dentition-stage classification, and the identification of clinical conditions such as crowding, missing teeth, prosthetics, and caries. Compared with supervised pipelines and prompted VLM baselines, ArchMap achieves higher accuracy, reduced semantic drift, and superior stability under sparse or artifact-prone conditions. As a fully training-free system, ArchMap demonstrates that combining geometric normalization with ontology-guided multimodal reasoning offers a practical and scalable solution for the structured analysis of 3D intraoral scans in modern digital orthodontics.

Problem

• Standardizing raw 3D dental meshes with varying arch poses and incomplete geometry
• Eliminating dependency on large annotated datasets and modality-specific training
• Enabling robust structured dental analysis across different devices and clinical conditions

Innovation

• Geometry-aware arch-flattening module standardizes 3D meshes
• Dental Knowledge Base encodes hierarchical tooth ontology
• Training-free framework combines geometric normalization with multimodal reasoning