Evidence map›Paper›PMID 33095178›Full record

SynthesisJournal of medical Internet research2020

Accuracy of Mobile Device-Compatible 3D Scanners for Facial Digitization: Systematic Review and Meta-Analysis.

Hang-Nga Mai, Du-Hyeong Lee

Open access · goldAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 3% of its field
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

23 citing papers in PubMed, 2 syntheses or guidelines pooled it, 61 citations in OpenAlex.

  1. Pooled it
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  8. [Method of constructing 3D facial smile simulation sequence data based on non-rigid registration].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2026
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  10. Snap-On PMMA Provisional Restorations in a Full Digital Workflow for the Rehabilitation of Worn Dentition: A Clinical Report.Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.] · 2026
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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

2 authors at 1 institution in 1 country.

Hang-Nga MaiInstitute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea.ORCID 0000-0002-9832-3312
Du-Hyeong LeeInstitute for Translational Research in Dentistry, Kyungpook National University, Daegu, Republic of Korea.ORCID 0000-0003-2803-7457
Kyungpook National University · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe accurate assessment and acquisition of facial anatomical information significantly contributes to enhancing the reliability of treatments in dental and medical fields, and has applications in fields such as craniomaxillofacial surgery, orthodontics, prosthodontics, orthopedics, and forensic medicine. Mobile device-compatible 3D facial scanners have been reported to be an effective tool for clinical use, but the accuracy of digital facial impressions obtained with the scanners has not been explored.

objectiveWe aimed to review comparisons of the accuracy of mobile device-compatible face scanners for facial digitization with that of systems for professional 3D facial scanning.

methodsIndividual search strategies were employed in PubMed (MEDLINE), Scopus, Science Direct, and Cochrane Library databases to search for articles published up to May 27, 2020. Peer-reviewed journal articles evaluating the accuracy of 3D facial models generated by mobile device-compatible face scanners were included. Cohen d effect size estimates and confidence intervals of standardized mean difference (SMD) data sets were used for meta-analysis.

resultsBy automatic database searching, 3942 articles were identified, of which 11 articles were considered eligible for narrative review, with 6 studies included in the meta-analysis. Overall, the accuracy of face models obtained using mobile device-compatible face scanners was significantly lower than that of face models obtained using professional 3D facial scanners (SMD 3.96 mm, 95% CI 2.81-5.10 mm; z=6.78; P<.001). The difference between face scanning when performed on inanimate facial models was significantly higher (SMD 10.53 mm, 95% CI 6.29-14.77 mm) than that when performed on living participants (SMD 2.58 mm, 95% CI 1.70-3.47 mm, P<.001, df=12.94).

conclusionsOverall, mobile device-compatible face scanners did not perform as well as professional scanning systems in 3D facial acquisition, but the deviations were within the clinically acceptable range of <1.5 mm. Significant differences between results when 3D facial scans were performed on inanimate facial objects and when performed on the faces of living participants were found; thus, caution should be exercised when interpreting results from studies conducted on inanimate objects.

Indexed as

AdultComputers, HandheldFaceFemaleHumansImaging, Three-DimensionalMaleMiddle AgedReproducibility of ResultsYoung Adultaccuracyfacial digitizationfacial scannersmeta-analysissystematic review

Identifiers

PMID33095178
PMCPMC7647818
OpenAlexW3091271879

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.