Journal of Dentistry· 2026Q1
Contact and penetration patterns in digital jaw-motion workflows: a within-case comparison
- 0citations
- Q1SCImago
- 2026year
Short summary
AI-driven jaw-motion simulation (AI-JM) resulted in a significantly smaller software-rendered contact/penetration footprint (median CAF 2.19%) compared to average-value virtual articulation (IOS-AV, 3.56%) and axiography-programmed virtual articulation (AX, 3.01%) in digital monolithic restoration workflows.
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Key points
- AI-driven jaw-motion simulation (AI-JM) yielded the lowest contact area fraction (CAF) in 12 out of 14 cases.
- Median CAF was 2.19% for AI-JM, 3.01% for AX, and 3.56% for IOS-AV.
- AI-JM showed significantly lower CAF than both AX and IOS-AV (p=0.04, Holm-adjusted).
- Findings relate to geometric compatibility, not patient-specific movement accuracy.
AI-generated from the title and abstract; the full text is not read.
Abstract
Background In fully digital workflows for monolithic restorations, virtual articulation combines interarch alignment with simulated movement. Wear-facet–informed AI estimates movement paths from intraoral scan morphology. This exploratory within-case study compared three workflows’ software-rendered contact/penetration footprints using Contact Area Fraction (CAF). Methods Fourteen paired intraoral scan datasets underwent exocad processing using IOS-derived maximal intercuspal position (MIP) with average-value virtual articulation (IOS-AV; a common chairside digital reference), AI-derived MIP with axiography-programmed virtual articulation (AX), and the same AI-derived MIP with AI jaw-motion file–driven simulation (AI-JM). A pre-specified Python pipeline quantified CAF (%) as the proportion of red plus magenta pixels within the mapped region of exported color-coded 3D proximity/contact PDFs. Paired within-case comparisons used Friedman and post-hoc Wilcoxon signed-rank tests with Holm correction. Results CAF differed significantly among workflows (Friedman χ²(2)=13.86, p=0.001; Kendall’s W=0.495). Median (IQR) CAF was 2.19% (1.59–3.67) for AI-JM, 3.01% (2.40–4.20) for AX, and 3.56% (2.57–4.50) for IOS-AV. AI-JM yielded the lowest CAF in 12/14 cases and significantly lower CAF than AX and IOS-AV (both Holm-adjusted p=0.04); AX and IOS-AV did not differ significantly. Conclusions AI-JM showed a smaller combined contact/penetration footprint than the tested comparators under the study conditions. IOS-AV comparisons reflect differences in both initial alignment and movement simulation; AX and AI-JM shared identical AI-derived alignment. Findings concern geometric compatibility with recorded morphology, not independently validated patient-specific movement accuracy. Clinical Significance The smaller software-rendered contact/penetration footprint highlights AI-JM’s potential to complement virtual occlusal assessment when planning monolithic restorations.
The authors' abstract, as published at the source. Journal of Dentistry, 2026 · DOI ↗
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Field: Orthodontics
OrthodonticsDentistry