Morphological effects of input data quantity in AI-powered dental crown design

URN to cite this document:: urn:nbn:de:bvb:355-epub-767729
DOI to cite this document:: 10.5283/epub.76772

Broll, Alexander ; Goldhacker, Markus ; Hahnel, Sebastian ; Rosentritt, Martin

License: Creative Commons Attribution 4.0
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Date of publication of this fulltext: 04 Jun 2025 09:58

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Abstract

Abstract

Objectives:
This retrospective in vitro study evaluated the impact of input data quantity on the morphology of dental crowns generated by AI-based software. The hypothesis suggests that increased input data quantity improves the quality of generated occlusal surfaces.
Methods:
A dataset comprising n=30 patients (11 males, 19 females; age: 22–31 years) was analyzed. Input data was categorized into full dentition (full), quadrant data (quad), and adjacent teeth (adj). AI-based software (Dentbird Crown, Imageworks Inc.) generated crowns for a single lower first molar (36/46). Metrics were proposed to assess the morphology and occlusal relationships of the crowns, with the original tooth as reference. Statistics: Friedman Chi-Square tests, Wilcoxon signed rank tests, Kendall correlation and Fligner–Killeen tests (α= 0.05).
Results:
Full and quad groups provided consistent reconstruction quality with no significant differences in morphology and occlusal relationships. The adj group showed significant () morphological deviations and higher reconstruction failure rates compared to the full and quad groups. Correlations (median: 0.19; min–max range: 0.01–0.54) indicate that the proposed metrics capture distinct morphological and functional crown aspects.
Conclusion:
The software reliably reconstructed crowns with at least quadrant-level input data. Performance declined with reduced input. Full-jaw scans did not enhance accuracy compared to quadrant data.
Clinical Significance:
Increased input data quantity can improve the accuracy of AI-based restorations. As a result, prosthodontists benefit from predictable, accurate restoration proposals that reduce the need for digital chairside adjustments as well as manual modifications after fabrication. This streamlines clinical workflows and enhances the quality of restorations. Quadrant-level data has proven sufficient to generate high-quality reconstructions. Further input data did not significantly improve the accuracy of the reconstructions. The proposed metrics enable quantitative assessments of morphological and functional restoration quality, supporting reliable AI-driven workflows.

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