Evidence map›Paper›PMID 41771190›Full record

ArticleInternational dental journal2026

Evaluation of the Effectiveness of an AI-assisted, Dual-Template Workflow in Improving the Accuracy of Tooth Autotransplantation.

Guangwei Chen, Zuwen Ma, Yudong Liu, Xuelian Wang, Weifa Li, Libin Zhou

Abstract read
In one paragraph

Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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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

6 authors.

Guangwei ChenDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Zuwen MaDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Yudong LiuDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Xuelian WangDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Weifa LiDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China.
Libin ZhouDepartment of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Guangdong Engineering Research Center of Oral Restoration and Reconstruction & Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangzhou Medical University, Guangzhou, China. Electronic address: fmmumario@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo test whether an AI-assisted, dual-template workflow improves plan-delivery accuracy in tooth autotransplantation versus a replica-only freehand approach.

methodsTwenty-four consecutive cases were assigned to a guided workflow (n = 12) or freehand (n = 12). In the guided group, AI-driven CBCT segmentation informed a socket-preparation guide, a socket-margin guide, and a printed donor-tooth replica; the freehand group used the replica alone. Immediate postoperative CBCT was rigidly registered to the plan. The prespecified primary endpoint was buccolingual angular deviation (BLAD). Secondary, exploratory endpoints included centre deviation distance (CDD), index-point distances at crown and root (IPDD-C/IPDD-R), long-axis (LAD), mesiodistal (MDAD) and rotational (RAD) deviations, and Dice similarity coefficient (DSC). Group differences were tested with t-tests or Mann-Whitney U tests.

resultsThe guided workflow yielded significantly lower BLAD than the freehand approach. Secondary analyses showed concordant reductions in rotational deviation and linear centring errors, with greater volumetric plan-achievement overlap. Long-axis deviation showed a modest, non-significant improvement, and mesiodistal deviation did not differ between groups.

conclusionsAn AI-assisted dual-template workflow improves plan-delivery fidelity in tooth autotransplantation, with the largest gains in buccolingual orientation and rotational control, alongside better coronal centring and increased volumetric concordance. Apical deviation was also reduced, whereas mesiodistal alignment was unchanged. CLINICAL SIGNIFICANCE: By constraining trajectory and socket margins, the guided workflow standardizes preparation and supports more predictable positioning of the transplanted tooth.

Indexed as

Artificial IntelligenceSurgery, Computer-AssistedToothWorkflowAdultCone-Beam Computed TomographyFemaleHumansIntelligent SystemsMaleTooth SocketTransplantation, AutologousAccuracyArtificial intelligenceCone-beam computed tomographyDigital dentistrySurgical guidesTooth autotransplantation

Identifiers

PMID41771190
PMCPMC12966716

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LicenceCC BY-NC-ND
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Registered trials

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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.