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Fit accuracy of complete crowns fabricated by generative artificial intelligence design: a comparative clinical study
Journal article   Open access   Peer reviewed

Fit accuracy of complete crowns fabricated by generative artificial intelligence design: a comparative clinical study

Thaw Thaw Win, Hang-Nga Mai, So-Yeun Kim, Seok-Hwan Cho, Jong-Eun Kim, Viritpon Srimaneepong, Jekita Kaenploy and Du-Hyeong Lee
The journal of advanced prosthodontics, Vol.17(4), pp.224-234
08/2025
DOI: 10.4047/jap.2025.17.4.224
PMCID: PMC12411301
PMID: 40919046
url
https://doi.org/10.4047/jap.2025.17.4.224View
Published (Version of record) Open Access

Abstract

PURPOSE Designing restorations remains challenging because the process is time-consuming and requires operator skill and experience. This clinical study evaluated the fit accuracy of polymerized complete crowns fabricated using a web-based 3D generative artificial intelligence design (GAID) method compared to crowns fabricated using a conventional computer-aided design (CCAD) method. MATERIALS AND METHODS Sixty-two patients requiring complete crowns in maxillary and mandibular premolars and molars were enrolled. After tooth preparation, digital impressions were taken using an intraoral scanner. Two crowns per patient were designed: one used a web-based automatic 3D GAID software program, and the other used a standard human-driven CCAD software program. The crowns were 3D-printed and delivered to the patients. Marginal and internal discrepancies and occlusal contacts were evaluated using a digital triple scan technique. Statistical analysis used two one-sided t-tests for paired samples to assess crown accuracy in both methods (α = .05). RESULTS Marginal gaps of crowns made by both methods showed equivalence in the buccal, mesial, and distal regions; however, in the lingual region, the GAID method produced higher marginal discrepancies (P > .001). Regarding internal gaps, no significant difference was observed between the two methods. Crowns produced by the GAID method exhibited larger occlusal discrepancies than those made by the CCAD method (P < .001). CONCLUSION The fit accuracy of crowns fabricated using generative artificial intelligence was equivalent to those produced using the manual-input computer design method when the margins were well defined. While marginal and occlusal discrepancies were within clinically acceptable range, careful attention must be given to automated design outcomes, considering various tooth preparation shapes, anatomical structures, and clinical variations.
Accuracy Artificial intelligence Complete crown Design Fit

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