Evidence map›Paper›PMID 40061025›Full record

ArticleThe journal of advanced prosthodontics2025

Verification of the accuracy and design time of crowns designed with artificial intelligence.

Koudai Nagata, Erika Inoue, Toshifumi Nakashizu, Kazuhide Seimiya, Mihoko Atsumi, Katsuhiko Kimoto, Shinji Kuroda, Noriyuki Hoshi

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in The journal of advanced prosthodontics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Review
  4. Review
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Koudai NagataDepartment of Regenerative Implant Dentistry, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0000-0002-8464-7340
Erika InoueDivision of the Dental Practice Support Department of Dental Technology, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0009-0000-7527-5576
Toshifumi NakashizuDivision of the Dental Practice Support Department of Dental Technology, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0009-0004-0363-6052
Kazuhide SeimiyaDivision of the Dental Practice Support Department of Dental Technology, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0009-0004-5500-2879
Mihoko AtsumiDepartment of Regenerative Implant Dentistry, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0009-0000-1624-9618
Katsuhiko KimotoDepartment of Fixed Prosthodontics, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0000-0003-3138-306X
Shinji KurodaDepartment of Regenerative Implant Dentistry, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0000-0003-3100-8243
Noriyuki HoshiDepartment of Oral Digital Science, Kanagawa Dental University, Yokosuka, Japan.ORCID https://orcid.org/0000-0001-7127-3995

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis study examines the potential of computer-aided design (CAD) systems equipped with artificial intelligence (AI) in reducing the workload of dental technicians. We aimed to compare the accuracy and design time of crowns designed using conventional CAD with those designed using AI-equipped CAD. MATERIALS AND

methodsAbutment tooth models of a maxillary right second premolar (FDI classification #15) and a maxillary left first molar (FDI classification #26) were mounted on a dental model to form the master model. Stereolithography data were acquired using an intraoral scanner, and five dental technicians designed one crown each for #15 and #26 using both conventional and AI-equipped CAD systems. With the #15 and #26 crowns, six measuring points were established for comparing the accuracy of the occlusal surfaces and design time of the crowns designed by the two CAD systems. The occlusal surfaces were also compared for the buccal and palatal sides.

resultsThe accuracy of the occlusal surface was 275.5 ± 116.8 µm and 25.7 ± 13 µm for the conventional CAD and AI-equipped CAD systems, respectively. For the buccal and palatal surface comparisons, the conventional CAD system revealed larger misfits on the palatal side for both #15 and #26, with significant differences observed. No significant differences were noted with the AI-equipped CAD system. The AI-equipped CAD resulted in significantly faster design time for both #15 and #26.

conclusionThe AI-based CAD system significantly reduced design time and enabled the fabrication of uniform crowns regardless of the dental technician's experience and skill.

Indexed as

Artificial intelligenceComputer-aided designCrownsDental models

Identifiers

PMID40061025
PMCPMC11886404

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.