Related Experiment Video
Updated: Jul 8, 2026
Measuring Maxillary Posterior Tooth Movement: A Model Assessment using Palatal and Dental Superimposition
Published on: February 23, 2024
Feasibility and accuracy of single maxillary molar designed by an implicit neural network (INN)-based model: A
Yiqing Wang1, Yuze Shi2, Nan Li2
1Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center of Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, Beijing, PR China.
Purpose:
This feasibility study aimed to develop and evaluate a novel implicit neural network (INN)-based model for the automatic reconstruction of biomimetic maxillary molar.
Material And Methods:
A total of 500 sets of full dental scans containing intact right and left maxillary first molars (#3 and #14) and adjacent teeth were included in this study. The digital maxillary casts were duplicated: one set served as the original, while the other had one maxillary first molar removed. Two INN-based models were developed, including the point convolution for surface reconstruction (POCO-only) model and the POCO with point multilayer perceptron (POCO-PointMLP) model. Each model was trained with either 12,000 or 50,000 sampling points from the training dataset. Chamfer distance (CD), F-score, and Volumetric Intersection over Union (volumetric IoU) were used to evaluate the INN-based models and training outcomes. The best INN-based model was selected to generate 20 digital crown designs (GCs) automatically from randomly selected test datasets and compared with the digital crowns designed by experienced human laboratory technicians (TCs). Both GCs and TCs were compared with the original clinical crowns (OCs) using root mean square (RMS) and color maps. Comparisons of RMS values were analyzed utilizing paired t-tests with statistical significance set at α = 0.05.
Results:
The performance of 2 INN-based models and training outcomes were evaluated and POCO-PointMLP trained with 50,000 sample points showed the best outcomes, with CD of 0.000119403, F-score of 0.919740566, and volumetric IoU of 0.943382978. This INN-based model was then selected to generate 20 digital crown designs (GCs) and compared with TCs for the RMS outcomes. The GCs exhibited numerically lower mean RMS compared to TCs without significant difference (0.2839 ± 0.0307 versus 0.3026 ± 0.0587 mm, p = 0.202). GCs and TCs both closely matched to the original clinical crowns (OCs).
Conclusions:
This study demonstrated the feasibility and accuracy of the proposed INN-based POCO-PointMLP model for automating the digital crown designs of maxillary first molars. The INN-based model-generated digital crown designs showed comparable 3D deviations to the ones designed by experienced human laboratory technicians, presenting a novel and efficient artificial neural network architecture for tooth digital design tasks with the expansion of training datasets, refined training hyperparameters, and integrated comprehensive assessments.
