Evaluation of the marginal and internal fit of CAD/CAM crowns designed using three different dental CAD programs: a 3-dimensional digital analysis study

Farah, Ra’fat I.; Alresheedi, Bandar

doi:10.1007/s00784-022-04720-6

Clin Oral Invest

2022

DOI: 10.1007/s00784-022-04720-6

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Evaluation of the marginal and internal fit of CAD/CAM crowns designed using three different dental CAD programs: a 3-dimensional digital analysis study

Ra’fat I. Farah

Bandar Alresheedi

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Cited by 4 publications

References 32 publications

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Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation

Jeong,

Bae,

Jang

et al. 2024

Sci Rep

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Traditional dental prosthetics require a significant amount of work, labor, and time. To simplify the process, a method to convert teeth scan images, scanned using an intraoral scanner, into 3D images for design was developed. Furthermore, several studies have used deep learning to automate dental prosthetic processes. Tooth images are required to train deep learning models, but they are difficult to use in research because they contain personal patient information. Therefore, we propose a method for generating virtual tooth images using image-to-image translation (pix2pix) and contextual reconstruction fill (CR-Fill). Various virtual images can be generated using pix2pix, and the images are used as training images for CR-Fill to compare the real image with the virtual image to ensure that the teeth are well-shaped and meaningful. The experimental results demonstrate that the images generated by the proposed method are similar to actual images. In addition, only using virtual images as training data did not perform well; however, using both real and virtual images as training data yielded nearly identical results to using only real images as training data.

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Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation

Jeong,

Bae,

Jang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Requirements for Dental CAD Software: A Survey of Korean Dental Personnel

Lee

Kim

et al. 2023

Applied Sciences

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This study aimed to evaluate the needs of dentists, dental technicians, dental hygienists, and dental-related workers in terms of dental computer-aided design (CAD) software and artificial intelligence (AI). Based on a factor analysis, 57 survey items were assigned to six categories: (a) considerations when purchasing dental CAD software; (b) prosthesis design process; (c) dental CAD function; (d) use of AI dental CAD crown and denture design; (e) impact of AI; and (f) improvements in AI features. Overall, 93 participants were included in the study, and the reliability of the resultant survey data was estimated using Cronbach’s alpha coefficient. Statistically significant factors in each category were identified using one-way analysis of variance and Tukey’s honestly significant difference test (α = 0.05). The results revealed that design quality, design convenience and reproducibility, margin line setting, and automatic margin setting were considered most important in their respective categories (p < 0.05). There were also significant differences in the importance of certain items, such as branding importance and functional diversity, among different dental personnel groups (p < 0.05). Design speed and convenience were also found to be more important to dentists and dental hygienists compared to other dental personnel (p < 0.05). The importance of specific survey items varied significantly based on age, dental personnel, and work experience groups. Dental personnel, such as dentists and dental technicians, assigned greater importance to certain factors, such as branding, functional diversity, design speed, and compatibility with CAM equipment, compared to other occupational groups.

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Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth

Alsheghri,

Ladini,

Hosseinimanesh

et al. 2024

Applied Sciences

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During a crown generation procedure, dental technicians depend on commercial software to generate a margin line to define the design boundary for the crown. The margin line generation remains a non-reproducible, inconsistent, and challenging procedure. In this work, we propose to generate margin line points on prepared teeth meshes using adaptive point learning inspired by the AdaPointTr model. We extracted ground truth margin lines as point clouds from the prepared teeth and crown bottom meshes. The chamfer distance (CD) and infoCD loss functions were used for training a supervised deep learning model that outputs a margin line as a point cloud. To enhance the generation results, the deep learning model was trained based on three different resolutions of the target margin lines, which were used to back-propagate the losses. Five folds were trained and an ensemble model was constructed. The training and test sets contained 913 and 134 samples, respectively, covering all teeth positions. Intraoral scanning was used to collect all samples. Our post-processing involves removing outlier points based on local point density and principal component analysis (PCA) followed by a spline prediction. Comparing our final spline predictions with the ground truth margin line using CD, we achieved a median distance of 0.137 mm. The median Hausdorff distance was 0.242 mm. We also propose a novel confidence metric for uncertainty quantification of generated margin lines during deployment. The metric was defined based on the percentage of removed outliers during the post-processing stage. The proposed end-to-end framework helps dental professionals in generating and evaluating margin lines consistently. The findings underscore the potential of deep learning to revolutionize the detection and extraction of 3D landmarks, offering personalized and robust methods to meet the increasing demands for precision and efficiency in the medical field.

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Evaluation of the marginal and internal fit of CAD/CAM crowns designed using three different dental CAD programs: a 3-dimensional digital analysis study

Cited by 4 publications

References 32 publications

Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation

Study on virtual tooth image generation utilizing CF-fill and Pix2pix for data augmentation

Requirements for Dental CAD Software: A Survey of Korean Dental Personnel

Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth

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