Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study.
Artificial intelligence-powered
Computer-aided
Crown
Morphological accuracy
Time efficiency
Journal
International dental journal
ISSN: 1875-595X
Titre abrégé: Int Dent J
Pays: England
ID NLM: 0374714
Informations de publication
Date de publication:
28 Jul 2024
28 Jul 2024
Historique:
received:
15
05
2024
revised:
20
06
2024
accepted:
24
06
2024
medline:
29
7
2024
pubmed:
29
7
2024
entrez:
28
7
2024
Statut:
aheadofprint
Résumé
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. 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). 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). 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.
Identifiants
pubmed: 39069456
pii: S0020-6539(24)00196-5
doi: 10.1016/j.identj.2024.06.023
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.