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Objective <p>The integration of artificial intelligence (AI) into CAD/CAM workflows has revolutionized dental prosthetics manufacturing, yet its morphological trueness compared to manual design remains underexplored.</p> Materials and methods <p>This 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 <i>p</i> &lt; 0.05.</p> Results <p>While 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&#xa0;μm) and manual designs (median = 68.6&#xa0;μm; <i>p</i> = 0.1056). However, AI designs produced significantly greater maximum discrepancies (mean = 225.0&#xa0;μm) compared to manual designs (mean = 184.4&#xa0;μm; <i>p</i> = 0.0243).</p> Conclusion <p>These 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.</p>

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Morphological comparison between artificial intelligence-driven and manual CAD design in single tooth restoration: a preliminary study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月17日

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  • Bing-ying Xie,
  • Xiao He,
  • Li Hu,
  • Shan-lin Guo,
  • Jun-lan Chen,
  • Jing Zhang,
  • Xiao-qing Shen,
  • Yuan-ming Geng,
  • Wei Li

摘要

Objective

The 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 methods

This 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.

Results

While 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).

Conclusion

These 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.