UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction

TL;DR

UniDCF is a novel multimodal deep learning framework that significantly improves the accuracy and efficiency of reconstructing multiple dentocraniofacial hard tissues, facilitating personalized treatment planning.

Contribution

It introduces a unified multimodal model for comprehensive dentocraniofacial tissue reconstruction, overcoming limitations of single-modality approaches and utilizing a large curated dataset.

Findings

Outperforms state-of-the-art methods in geometric precision and structural completeness.

Reduces clinical reconstruction time by 99%.

Achieves over 94% clinician-rated acceptability.

Abstract

Dentocraniofacial hard tissue defects profoundly affect patients' physiological functions, facial aesthetics, and psychological well-being, posing significant challenges for precise reconstruction. Current deep learning models are limited to single-tissue scenarios and modality-specific imaging inputs, resulting in poor generalizability and trade-offs between anatomical fidelity, computational efficiency, and cross-tissue adaptability. Here we introduce UniDCF, a unified framework capable of reconstructing multiple dentocraniofacial hard tissues through multimodal fusion encoding of point clouds and multi-view images. By leveraging the complementary strengths of each modality and incorporating a score-based denoising module to refine surface smoothness, UniDCF overcomes the limitations of prior single-modality approaches. We curated the largest multimodal dataset, comprising intraoral scans, CBCT, and CT from 6,609 patients, resulting in 54,555 annotated instances. Evaluations demonstrate that UniDCF outperforms existing state-of-the-art methods in terms of geometric precision, structural completeness, and spatial accuracy. Clinical simulations indicate UniDCF reduces reconstruction design time by 99% and achieves clinician-rated acceptability exceeding 94%. Overall, UniDCF enables rapid, automated, and high-fidelity reconstruction, supporting personalized and precise restorative treatments, streamlining clinical workflows, and enhancing patient outcomes.