ArticleBMC oral health2025
Morphological comparison between artificial intelligence-driven and manual CAD design in single tooth restoration: a preliminary study.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Deep learning-based automated detection of Micro-cracks in monolithic zirconia crowns using Micro-CT imaging: AnBioinformation · 2026Article
- Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis.Biomedical engineering and computational biology · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveThe integration of artificial intelligence (AI) into CAD/CAM workflows has revolutionized dental prosthetics manufacturing, yet its morphological trueness compared to manual design remains underexplored. MATERIALS AND
methodsThis study evaluated 30 single-tooth restoration cases from 30 patients. For each case, the original clinically-approved designs were used as reference. AI designs (3Shape Automate) were compared to manual designs created by a technician (3Shape Dental System™). Morphological trueness was evaluated through 3D deviation analysis. Global surface deviations (RMSE) were compared using the Wilcoxon signed-rank test, and maximum discrepancies were compared with a paired Student's t-test, with significance set at p < 0.05.
resultsWhile AI demonstrated batch-processing efficiency, 6.7% of cases (2/30) with suboptimal preparation geometries required manual intervention. No significant difference was found in global surface deviation between AI (median = 79.8 μm) and manual designs (median = 68.6 μm; p = 0.1056). However, AI designs produced significantly greater maximum discrepancies (mean = 225.0 μm) compared to manual designs (mean = 184.4 μm; p = 0.0243).
conclusionThese findings validate AI's viability for routine restoration design but emphasize the necessity of case selection protocols and algorithm improvements for dynamic occlusion modeling to ensure comprehensive clinical adoption.
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Registered trials
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