Evidence map›Paper›PMID 41843229›Full record

ArticleClinical oral investigations2026

Evaluating the feasibility of intraoral scanning technology for removable partial dentures: ai-assisted measurement analysis and clinical investigation.

Xinyu Yang, Yumin Wu, Mengke Qian, Ruoyun Wu, Haifeng Xie, Chen Chen

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Article in Clinical oral investigations, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Xinyu Yang *Department of Prosthodontics, the Affiliated Stomatology Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing, 210029, China.
Yumin Wu *Stomatological Hospital Affiliated Suzhou Vocational Health College, Suzhou, 215002, China.
Mengke QianDepartment of Endodontics, the Affiliated Stomatology Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing, 210029, China.
Ruoyun WuDepartment of Prosthodontics, the Affiliated Stomatology Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing, 210029, China.
Haifeng XieDepartment of Prosthodontics, the Affiliated Stomatology Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing, 210029, China. hfxie@njmu.edu.cn.ORCID http://orcid.org/0000-0003-2008-3846
Chen ChenDepartment of Endodontics, the Affiliated Stomatology Hospital of Nanjing Medical University; State Key Laboratory Cultivation Base of Research, Prevention and Treatment for Oral Diseases (Nanjing Medical University); Jiangsu Province Engineering Research Center of Stomatological Translational Medicine (Nanjing Medical University), Nanjing, 210029, China. ccchicy@njmu.edu.cn.ORCID http://orcid.org/0000-0002-2989-180X

Funding

Health Research Project of Jiangsu Commission of Health grant BJ24025Jiangsu Provincal Medical Innovation Center CXZX202227Jiangsu Province Capability Improvement Project through Science,Technology and Education-Jiangsu Provincal Research Hospital Cultivation Unit YJXYYJSDW4
6 · The paper itself

Abstract

objectivesTo evaluate the feasibility of intraoral scanning (IOS) for the digital design and fabrication of removable partial dentures (RPDs) across different Kennedy classes, and to compare IOS-based models with conventional final impressions using reverse engineering and artificial intelligence (AI)-assisted analyses. The null hypothesis was that no statistically significant differences exist in 3D deviations between IOS and conventional impression models among Kennedy classes. A secondary hypothesis was that AI-assisted analysis could serve as a reliable supplement to conventional reverse engineering. MATERIALS AND

methodsFifty partially edentulous patients underwent two methods. 3D deviations between corresponding maxillary and mandibular models were quantified using reverse engineering software. Subgroup analyses were performed according to Kennedy classification, patient-related factors, and anatomical regions. A PointNet++-based deep learning model was used exclusively for automated segmentation and alignment. Data normality was assessed using Shapiro-Wilk test. One-way or two-way ANOVA with Tukey’s post hoc test was applied. Clinical follow-up was conducted at 3, 6, and 12 months.

resultsSmaller deviations were observed in Kennedy Class III and IV cases, whereas larger deviations occurred in Class I and II cases, particularly in soft tissue regions such as the palate, gingival margins, and retromolar pads. Age and sex showed minimal influence, while higher Body Mass Index (BMI) was associated with increased soft tissue deviation. AI-assisted analysis produced results consistent with conventional reverse engineering.

conclusionsIOS demonstrates stable performance in Kennedy Class III and IV cases and acceptable outcomes in selected Class I and II cases with appropriate clinical adjustment. CLINICAL RELEVANCE: IOS may be applied for RPD fabrication in carefully selected cases; however, free-end extension RPDs still require cautious case selection and post-insertion adjustment.

Indexed as

Artificial IntelligenceComputer-Aided DesignDenture DesignDenture, Partial, RemovableImaging, Three-DimensionalJaw, Edentulous, PartiallyAgedDental Impression TechniqueFeasibility StudiesFemaleHumansIntelligent SystemsMaleMiddle AgedModels, DentalArtificial intelligenceDeep learningDeviationDigital removable partial denturesIntraoral scanning

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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.