Assessment of the quantitative impact of occlusal positioning splints on temporomandibular joint conditions

• URL: http://arxiv.org/abs/2604.25322v1
• Date: Tue, 28 Apr 2026 07:37:16 GMT
• Title: Assessment of the quantitative impact of occlusal positioning splints on temporomandibular joint conditions
• Abstract summary: A computational method for quantitative analysis of temporomandibular joint (TMJ) configuration is proposed and demonstrated.<n>The method models a positioning splint as a physical realization of a predefined rigid transformation of the mandible.<n>Splints corresponding to selected mandibular positions are designed and fabricated, and their positioning accuracy is evaluated.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A computational method for quantitative analysis of temporomandibular joint (TMJ) configuration using occlusal positioning splints is proposed and demonstrated. The method models a positioning splint as a physical realization of a predefined rigid transformation of the mandible, derived from multimodal data, including CBCT, facial motion acquisition, and dental scans integrated within a common coordinate system. Splints corresponding to selected mandibular positions are designed and fabricated, and their positioning accuracy is evaluated using repeated scans of plaster models. Discrepancies are represented as error transformations and analyzed statistically in the space of rigid motions. The estimated transformations are propagated to segmented TMJ structures, enabling simulation-based evaluation of joint space changes. Transformation-based error analysis and surface distance metrics are used to quantify differences between planned and achieved configurations. The method enables indirect assessment of TMJ configuration using a single anatomical model and transformation data, reducing the need for repeated imaging across multiple mandibular positions. This study is intended as a methodological demonstration, supported by a clear step-by-step graphical presentation, and does not aim to provide clinical validation.

Related papers

• Test-time Adaptation of Pelvic Bone Segmentation Models via Dynamic Reliability-Guided [62.10276009853225]
  Reliable pelvic bone segmentation from CT is essential for robot-assisted pelvic trauma surgery.<n>Existing methods show limited effectiveness for PBS.<n>We propose a novel closed-loop dynamic Reliability-Guided framework (ReGA) for PBS.
  arXiv  Detail & Related papers  (2026-08-01T08:13:20Z)
• Towards Voxel Spacing Consistency for Medical Image Segmentation [56.992050146446495]
  We study the correlation between image resampling and segmentation, and propose Consispace, a semantic-aware resampling framework.<n> Consispace achieves consistent voxel spacing in the axial direction while preserving anatomical and semantic consistency.
  arXiv  Detail & Related papers  (2026-06-30T15:42:23Z)
• Transformation-driven generation of comparable projection images from multimodal anatomical scenes [0.0]
  We propose a transformation-driven framework for synthetic projection imaging from multimodal anatomical data.<n>Independently transformable volumetric and surface-based anatomical objects are embedded within a shared scene representation.<n>Projection geometry, acquisition modelling, material interpretation, and image presentation remain explicitly separated.
  arXiv  Detail & Related papers  (2026-06-15T11:19:10Z)
• Particle Diffusion Matching: Random Walk Correspondence Search for the Alignment of Standard and Ultra-Widefield Fundus Images [47.944896477309555]
  We propose a robust alignment technique for Standard Fundus Images (SFIs) and Ultra-Widefield Fundus Images (UWFIs)<n>Our method, termed Particle Diffusion Matching (PDM), performs alignment through an iterative Random Walk Correspondence Search (RWCS) guided by a diffusion model.<n>PDM achieves state-of-the-art performance across multiple retinal image alignment benchmarks.
  arXiv  Detail & Related papers  (2026-04-11T08:09:53Z)
• Independent Component Discovery in Temporal Count Data [46.526610368455096]
  We introduce a generative framework for independent component analysis of temporal count data, combining regime-adaptive dynamics with Poisson log-normal emissions.<n>The model identifies disentangled components with regime-dependent contributions, enabling representation learning and perturbations analysis.
  arXiv  Detail & Related papers  (2026-01-29T13:30:10Z)
• On Procrustes Contamination in Machine Learning Applications of Geometric Morphometrics [0.0]
  Geometric morphometrics (GMM) is widely used to quantify shape variation, more recently serving as input for machine learning (ML) analyses.<n>Standard practice aligns all specimens via Generalized Procrustes Analysis (GPA) prior to splitting data into training and test sets.<n>Here, the effects of GPA-induced contamination are formally characterised using controlled 2D and 3D simulations.
  arXiv  Detail & Related papers  (2026-01-26T12:56:23Z)
• From Variability To Accuracy: Conditional Bernoulli Diffusion Models with Consensus-Driven Correction for Thin Structure Segmentation [0.0]
  In ambiguous regions, existing segmentation approaches often output disconnected or under-segmented results.<n>We propose a novel framework that corrects segmentation results by leveraging consensus from multiple diffusion model outputs.<n>Our method automates the manual process of segmentation result correction and can be applied to image-guided surgical planning and surgery.
  arXiv  Detail & Related papers  (2025-07-17T10:44:06Z)
• Computer-Aided Design of Personalized Occlusal Positioning Splints Using Multimodal 3D Data [0.0]
  This study presents a computer-aided approach for designing and evaluating occlusal positioning splints.<n>The primary aim is to demonstrate the feasibility and geometric accuracy of the proposed method at the preclinical stage.
  arXiv  Detail & Related papers  (2025-04-17T11:53:49Z)
• Abdominal organ segmentation via deep diffeomorphic mesh deformations [5.4173776411667935]
  Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. We employ template-based mesh reconstruction methods for joint liver, kidney, pancreas, and spleen segmentation. The resulting method, UNetFlow, generalizes well to all four organs and can be easily fine-tuned on new data.
  arXiv  Detail & Related papers  (2023-06-27T14:41:18Z)
• Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots [0.0]
  A data-driven surrogate model is proposed for the efficient prediction of blood flow simulations on similar but distinct domains. A non-intrusive reduced-order model for geometrical parameters is constructed using proper decomposition. A radial basis function interpolator is trained for predicting the reduced coefficients of the reduced-order model.
  arXiv  Detail & Related papers  (2023-02-21T21:18:17Z)
• A kinetic approach to consensus-based segmentation of biomedical images [39.58317527488534]
  We apply a kinetic version of a bounded confidence consensus model to biomedical segmentation problems. The large time behavior of the system is then computed with the aid of a surrogate Fokker-Planck approach. We minimize the introduced segmentation metric for a relevant set of 2D gray-scale images.
  arXiv  Detail & Related papers  (2022-11-08T09:54:34Z)
• A Model for Multi-View Residual Covariances based on Perspective Deformation [88.21738020902411]
  We derive a model for the covariance of the visual residuals in multi-view SfM, odometry and SLAM setups. We validate our model with synthetic and real data and integrate it into photometric and feature-based Bundle Adjustment.
  arXiv  Detail & Related papers  (2022-02-01T21:21:56Z)
• Benchmarking off-the-shelf statistical shape modeling tools in clinical applications [53.47202621511081]
  We systematically assess the outcome of widely used, state-of-the-art SSM tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability.
  arXiv  Detail & Related papers  (2020-09-07T03:51:35Z)
• Appearance Learning for Image-based Motion Estimation in Tomography [60.980769164955454]
  In tomographic imaging, anatomical structures are reconstructed by applying a pseudo-inverse forward model to acquired signals. Patient motion corrupts the geometry alignment in the reconstruction process resulting in motion artifacts. We propose an appearance learning approach recognizing the structures of rigid motion independently from the scanned object.
  arXiv  Detail & Related papers  (2020-06-18T09:49:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.