Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study
Introduction and aims: Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software....
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | International Dental Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0020653924001965 |
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| Summary: | Introduction and aims: Artificial intelligence (AI) has been adopted in the field of dental restoration. This study aimed to evaluate the time efficiency and morphological accuracy of crowns designed by two AI-powered software programs in comparison with conventional computer-aided design software. Methods: A total of 33 clinically adapted posterior crowns were involved in the standard group. AI Automate (AA) and AI Dentbird Crown (AD) used two AI-powered design software programs, while the computer-aided experienced and computer-aided novice employed the Exocad DentalCAD software. Time efficiency between the AI-powered groups and computer-aided groups was evaluated by assessing the elapsed time. Morphological accuracy was assessed by means of three-dimensional geometric calculations, with the root-mean-square error compared against the standard group. Statistical analysis was conducted via the Kruskal–Wallis test (α = 0.05). Results: The time efficiency of the AI-powered groups was significantly higher than that of the computer-aided groups (P < .01). Moreover, the working time for both AA and AD groups was only one-quarter of that for the computer-aided novice group. Four groups significantly differed in morphological accuracy for occlusal and distal surfaces (P < .05). The AD group performed lower accuracy than the other three groups on the occlusal surfaces (P < .001) and the computer-aided experienced group was superior to the AA group in terms of accuracy on the distal surfaces (P = .029). However, morphological accuracy showed no significant difference among the four groups for mesial surfaces and margin lines (P > .05). Conclusion: AI-powered software enhanced the efficiency of crown design but failed to excel at morphological accuracy compared with experienced technicians using computer-aided software. AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design. |
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| ISSN: | 0020-6539 |