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arxiv: 2604.25322 · v1 · submitted 2026-04-28 · 💻 cs.CV

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

Agnieszka Anna Tomaka , Krzysztof Domino , Dariusz Pojda , Micha{\l} Tarnawski This is my paper

Pith reviewed 2026-05-07 16:59 UTC · model grok-4.3

classification 💻 cs.CV
keywords temporomandibular jointocclusal splintsrigid transformationCBCT imagingmandibular positioningerror propagationjoint space analysissimulation method
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The pith

Occlusal splints can be modeled as rigid transformations to quantify TMJ configuration changes from a single anatomical model.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops a computational method that treats occlusal positioning splints as physical realizations of predefined rigid transformations of the mandible derived from integrated CBCT, motion, and dental scan data. Splints are designed and fabricated for selected positions, after which their accuracy is tested through repeated scans of plaster models to derive statistical error transformations. These errors are then propagated to segmented TMJ structures to simulate and measure joint space alterations using surface distance metrics. The approach allows indirect evaluation across multiple mandibular positions without acquiring new images for each one. The work is presented as a methodological demonstration with step-by-step illustrations rather than a clinical trial.

Core claim

The central claim is that modeling a positioning splint as the physical embodiment of a rigid transformation allows the planned mandibular position to be realized in the patient, with any fabrication discrepancies captured as error transformations in rigid motion space. These transformations and their errors are applied to a single set of segmented TMJ structures obtained from one CBCT scan, enabling quantitative simulation of joint space changes via surface distance metrics between the planned and achieved configurations.

What carries the argument

Modeling the splint as a rigid transformation of the mandible with statistical propagation of error transformations in rigid motion space to compute TMJ joint space differences on one anatomical model.

If this is right

  • Planned versus achieved mandibular positions can be compared quantitatively through statistical analysis of rigid motion discrepancies.
  • Joint space alterations in the TMJ can be evaluated by propagating transformations to a single segmented anatomical model.
  • Surface distance metrics provide a concrete numerical measure of differences between intended and realized TMJ configurations.
  • The need for separate imaging sessions in each mandibular position is replaced by transformation-based simulation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Clinicians could test the mechanical effect of several candidate splint designs on TMJ space before any physical fabrication occurs.
  • The same transformation-error approach might apply to other intraoral devices where small positioning deviations alter joint loading.
  • Combining the method with higher-resolution motion tracking could tighten the bounds on how much error must be tolerated in splint manufacturing.
  • If the rigid-motion assumption holds across patients, the technique offers a route to personalized splint optimization based on simulated rather than trial-and-error outcomes.

Load-bearing premise

The errors introduced when a fabricated splint is placed in the mouth can be accurately measured and represented as rigid motions that then apply correctly to the segmented TMJ structures without significant non-rigid effects.

What would settle it

Acquire repeated CBCT scans of the same patient in the planned positions both with and without the fabricated splints, then compare the directly observed TMJ joint spaces against the spaces predicted by applying the measured error transformations to the single-model simulation.

Figures

Figures reproduced from arXiv: 2604.25322 by Agnieszka Anna Tomaka, Dariusz Pojda, Krzysztof Domino, Micha{\l} Tarnawski.

Figure 1
Figure 1. Figure 1: Groups of images used for registration in different coordinate systems. view at source ↗
Figure 2
Figure 2. Figure 2: Consistency of multimodal registration expressed through a trans view at source ↗
Figure 3
Figure 3. Figure 3: Diagram of the motion acquisition process (left). Maximal intercus view at source ↗
Figure 4
Figure 4. Figure 4: (A) Occlusal splint model designed for the selected maxilla–mandible view at source ↗
Figure 5
Figure 5. Figure 5: Visualization of the PCA ellipsoid (Table 2) for the translational view at source ↗
Figure 6
Figure 6. Figure 6: Statistical characterization of transformation error. The first row view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of errors. Left: histogram of mean signed distances across view at source ↗
Figure 8
Figure 8. Figure 8: Distance maps from the joint to the condyle for simulations at the view at source ↗
Figure 9
Figure 9. Figure 9: Mean signed distance differences between the condyle and the artic view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes a computational method for quantitative analysis of temporomandibular joint (TMJ) configuration changes using occlusal positioning splints. Splints are modeled as physical realizations of predefined rigid transformations derived from integrated multimodal data (CBCT, facial motion acquisition, dental scans). Positioning accuracy is evaluated via repeated scans of plaster models, with discrepancies represented statistically as error transformations in rigid-motion space. These transformations are propagated to segmented TMJ structures to simulate joint-space changes, using transformation-based error analysis and surface distance metrics. The work is explicitly framed as a methodological demonstration with graphical step-by-step presentation, intended to enable indirect TMJ assessment from a single anatomical model without repeated imaging across mandibular positions.

Significance. If the core assumptions hold, the method offers a potential reduction in imaging burden for TMJ studies by leveraging splint-induced rigid transformations and statistical error propagation in rigid-motion space. Strengths include the integration of multimodal data into a common coordinate system and the use of rigid-motion statistics for discrepancy analysis, which supports reproducible simulation of joint configurations. As a demonstration without clinical data or quantitative validation metrics, its significance is primarily methodological rather than immediately translational.

major comments (2)
  1. [Methods (validation and error propagation)] Methods section on validation and error analysis: Positioning accuracy is assessed exclusively through repeated scans of plaster models, with discrepancies represented as error transformations in rigid-motion space and propagated to segmented TMJ structures. This omits soft-tissue deformation, condylar mobility, and patient-specific intraoral factors, leaving untested the assumption that errors can be accurately modeled and propagated as small rigid perturbations for quantitative TMJ impact assessment. This is load-bearing for the central claim of enabling indirect assessment via splints.
  2. [Abstract and Results] Abstract and Results: No quantitative results, error distributions, validation metrics, or specific numerical outcomes from the plaster-model experiments or TMJ simulations are reported. The manuscript is positioned as a methodological demonstration, but this absence makes it impossible to evaluate the precision or practical effect size of the transformation-based analysis on joint-space metrics.
minor comments (2)
  1. [Abstract and Figures] The abstract states the work is 'supported by a clear step-by-step graphical presentation,' but the manuscript would benefit from explicit figure captions or a dedicated methods subsection detailing how rigid transformations are applied to TMJ surface meshes.
  2. [Methods] Notation for rigid motions and error transformations should be defined more explicitly (e.g., via a table of symbols) to aid readers unfamiliar with SE(3) statistics.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our methodological demonstration. We address each major comment below and outline revisions to improve clarity, transparency, and evaluability while preserving the paper's intended scope.

read point-by-point responses

  1. Referee: Methods section on validation and error analysis: Positioning accuracy is assessed exclusively through repeated scans of plaster models, with discrepancies represented as error transformations in rigid-motion space and propagated to segmented TMJ structures. This omits soft-tissue deformation, condylar mobility, and patient-specific intraoral factors, leaving untested the assumption that errors can be accurately modeled and propagated as small rigid perturbations for quantitative TMJ impact assessment. This is load-bearing for the central claim of enabling indirect assessment via splints.

    Authors: We agree that the validation relies on rigid plaster models and does not capture soft-tissue deformation, condylar mobility, or intraoral patient factors. This is a deliberate choice consistent with the manuscript's framing as a methodological demonstration of the multimodal registration pipeline, rigid-motion error modeling, and propagation to TMJ surfaces. The core assumption of small rigid perturbations is justified by the physical role of the splint in enforcing mandibular positioning. We will revise the Methods to more explicitly describe the experimental setup and add a Limitations paragraph in the Discussion that states these boundaries, explains the rationale for the rigid model, and outlines how the approach can serve as a foundation for future in vivo studies. This addresses the load-bearing nature of the assumption without overclaiming translational readiness. revision: yes

  2. Referee: Abstract and Results: No quantitative results, error distributions, validation metrics, or specific numerical outcomes from the plaster-model experiments or TMJ simulations are reported. The manuscript is positioned as a methodological demonstration, but this absence makes it impossible to evaluate the precision or practical effect size of the transformation-based analysis on joint-space metrics.

    Authors: The current manuscript prioritizes a graphical step-by-step presentation of the pipeline over tabulated numerical results. However, the plaster-model experiments and subsequent TMJ simulations do produce concrete error statistics (e.g., mean and variance of rigid transformations) and surface-distance metrics that can be summarized quantitatively. We will add a concise Results subsection reporting key numerical outcomes from the validation experiments and example joint-space change distributions. This will allow readers to assess precision and effect size while maintaining the demonstration-oriented framing. The Abstract will be updated to reference these additions. revision: yes

Circularity Check

0 steps flagged

No circularity; derivation relies on external multimodal data and standard rigid-motion propagation

full rationale

The paper integrates CBCT, facial motion, and dental scans into a common coordinate system to define planned rigid transformations, fabricates splints as physical realizations of those transformations, measures positioning discrepancies on plaster models via repeated scans, represents errors statistically in rigid-motion space, and propagates the transformations to segmented TMJ structures. No equations, fitted parameters, or central claims reduce by construction to the inputs; the error analysis uses standard rigid-motion statistics without self-referential definitions or load-bearing self-citations. The approach is a methodological demonstration that remains independent of its own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

Method rests on standard domain assumptions of rigid-body mandible motion and accurate multimodal registration; no free parameters or new entities are described in the abstract.

axioms (2)
  • domain assumption Mandible can be treated as a rigid body whose position is fully described by a rigid transformation
    Invoked when modeling the splint as a physical realization of a predefined rigid transformation.
  • domain assumption Multimodal data (CBCT, motion capture, dental scans) can be aligned into a single accurate common coordinate system
    Required for deriving the transformation used to design the splint.

pith-pipeline@v0.9.0 · 5498 in / 1215 out tokens · 38955 ms · 2026-05-07T16:59:08.778582+00:00 · methodology

discussion (0)

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Reference graph

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