Bone and soft tissue changes associated with a removable partial denture. A novel method with a fusion of CBCT and optical 3D images

Orofacial esthetics, chewing function, and oral health-related quality of life in Kennedy class I patients with mini-implant-retained removable partial dentures: A 3-year clinical prospective study

This prospective clinical study aimed to assess self-reported orofacial esthetics, chewing function, and oral health-related quality of life (OHRQoL) over three years in the Kennedy Class I patients without posterior dentition who received free-end saddle removable partial dentures (RPDs) retained by two mini dental implants (MDIs) inserted in the canine/first premolar region. The study's robust findings reaffirm the viability of MDI-retained RPDs as a treatment modality in contemporary prosthodontics, instilling confidence in the dental community.

MATERIALS AND METHODS

92 participants with posterior edentulism in the maxilla or mandible received 184 MDIs and 92 RPDs. After one year, three participants were excluded, and another seven were excluded after three years. The final sample was 82 participants. Self-perceived orofacial esthetics was assessed by the Orofacial Esthetic Scale (OES), chewing function by the Chewing Function Questionnaire (CFQ), and the OHRQoL by the OHIP-14. Statistical analysis utilized multivariate regression analysis, standardized effect size calculation, Wilcoxon Signed Rank test, and Friedman's test.

RESULTS

OHRQoL and chewing function significantly improved (p < 0.001) one month after MDI loading by the new RPDs and continued to improve over the observation period (p < 0.05). The OES also significantly improved (p < 0.001) and remained almost unchanged over the next three years (p = 0.440).

CONCLUSION

Despite the limitations of this study, the MDI-retained RPD appears to be a viable treatment modality in contemporary prosthodontics from the patients' perspective.

Intraoral Scanners for In Vivo 3D Imaging of the Gingiva and the Alveolar Process

This study aimed to assess the reliability of two intraoral surface scanners for the representation of the alveolar process in vivo. Complete maxillary scans (CS 3600, Carestream and TRIOS 3, 3Shape) were repeatedly obtained from 13 fully dentate individuals. Scanner precision and agreement were tested using 3D surface superimpositions on the following reference areas: the buccal front teeth area, the entire dental arch, the entire alveolar process, or single teeth by applying an iterative closest point algorithm. Following each superimposition, the mean absolute distance (MAD) between predefined 3D model surfaces was calculated. Outcomes were analyzed through non-parametric statistics and the visualization of color-coded distance maps. When superimpositions were performed on the alveolar process, the median scanner precision was below 0.05 mm, with statistically significant but negligible differences between scanners. The agreement between the scanners was approximately 0.06 mm. When single-tooth superimpositions were used to assess the precision of adjacent alveolar soft-tissue surfaces, the median error was 0.028 mm, and there was higher agreement between the scanners. The in vivo reliability of the intraoral scanners in the alveolar surface area was high overall. Single-tooth superimpositions should be preferred for the optimal assessment of neighboring alveolar surface areas relative to the dentition.

A diffeomorphic unsupervised method for deformable soft tissue image registration

BACKGROUND AND OBJECTIVES

The image registration methods for deformable soft tissues utilize nonlinear transformations to align a pair of images precisely. In some situations, when there is huge gray scale difference or large deformation between the images to be registered, the deformation field tends to fold at some local voxels, which will result in the breakdown of the one-to-one mapping between images and the reduction of invertibility of the deformation field. In order to address this issue, a novel registration approach based on unsupervised learning is presented for deformable soft tissue image registration.

METHODS

A novel unsupervised learning based registration approach, which consists of a registration network, a velocity field integration module and a grid sampling module, is presented for deformable soft tissue image registration. The main contributions are: (1) A novel encoder-decoder network is presented for the evaluation of stationary velocity field. (2) A Jacobian determinant based penalty term (Jacobian loss) is developed to reduce the folding voxels and to improve the invertibility of the deformation field.

RESULTS AND CONCLUSIONS

The experimental results show that a new pair of images can be accurately registered using the trained registration model. In comparison with the conventional state-of-the-art method, SyN, the invertibility of the deformation field, accuracy and speed are all improved. Compared with the deep learning based method, VoxelMorph, the proposed method improves the invertibility of the deformation field.