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A critical step in virtual dental treatment planning is to accuratelydelineate all tooth-bone structures from CBCT with high fidelity and accurateanatomical information. Previous studies have established several methods forCBCT segmentation using deep learning. However, the inherent resolutiondiscrepancy of CBCT and the loss of occlusal and dentition information largelylimited its clinical applicability. Here, we present a Deep Dental MultimodalAnalysis (DDMA) framework consisting of a CBCT segmentation model, an intraoralscan (IOS) segmentation model (the most accurate digital dental model), and afusion model to generate 3D fused crown-root-bone structures with high fidelityand accurate occlusal and dentition information. Our model was trained with alarge-scale dataset with 503 CBCT and 28,559 IOS meshes manually annotated byexperienced human experts. For CBCT segmentation, we use a five-fold crossvalidation test, each with 50 CBCT, and our model achieves an average Dicecoefficient and IoU of 93.99% and 88.68%, respectively, significantlyoutperforming the baselines. For IOS segmentations, our model achieves an mIoUof 93.07% and 95.70% on the maxillary and mandible on a test set of 200 IOSmeshes, which are 1.77% and 3.52% higher than the state-of-art method. Our DDMAframework takes about 20 to 25 minutes to generate the fused 3D mesh modelfollowing the sequential processing order, compared to over 5 hours by humanexperts. Notably, our framework has been incorporated into a software by aclear aligner manufacturer, and real-world clinical cases demonstrate that ourmodel can visualize crown-root-bone structures during the entire orthodontictreatment and can predict risks like dehiscence and fenestration. Thesefindings demonstrate the potential of multi-modal deep learning to improve thequality of digital dental models and help dentists make better clinicaldecisions.

AI-enabled Automatic Multimodal Fusion of Cone-Beam CT and Intraoral Scans for Intelligent 3D Tooth-Bone Reconstruction and Clinical Applications