ArticleScientific reports2020
Sources of variation in the 3dMDface and Vectra H1 3D facial imaging systems.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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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.
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.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Validation of Vectra 3D Imaging Systems: A Review.International journal of environmental research and public health · 2022Pooled it
- Accuracy of Portable Face-Scanning Devices for Obtaining Three-Dimensional Face Models: A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2020Pooled it
- Repeatability of Phoneme-Guided Versus Conventional Acquisition of Smiling and Rest-Related Facial States Using 3D Stereophotogrammetry: A Within-Participant Study.Bioengineering (Basel, Switzerland) · 2026Article
- How Sex Shapes Facial Morphology in Adults: A 3D Geometric Morphometric Study.Diagnostics (Basel, Switzerland) · 2026Article
- Advancing genotype-phenotype analysis through 3D facial morphometry: insights from Cri-du-Chat syndrome.Journal of medical genetics · 2026Article
- Are different photogrammetry applications on smartphones sufficiently reliable?Korean journal of orthodontics · 2025Article
- Smartphone-generated 3D facial images: reliable for routine assessment of the oronasal region of patients with cleft or mere convenience? A validation study.BMC oral health · 2024Article
- Frontiers in Three-Dimensional Surface Imaging Systems for 3D Face Acquisition in Craniofacial Research and Practice: An Updated Literature Review.Diagnostics (Basel, Switzerland) · 2024Review
- Geometric learning and statistical modeling for surgical outcomes evaluation in craniosynostosis using 3D photogrammetry.Computer methods and programs in biomedicine · 2023Article
- Decoding the Human Face: Progress and Challenges in Understanding the Genetics of Craniofacial Morphology.Annual review of genomics and human genetics · 2022Review
- Reproducibility of Novel Soft-Tissue Landmarks on Three-Dimensional Human Facial Scan Images in Caucasian and Asian.Aesthetic plastic surgery · 2022Article
- Large-scale open-source three-dimensional growth curves for clinical facial assessment and objective description of facial dysmorphism.Scientific reports · 2021Article
- 3D facial phenotyping by biometric sibling matching used in contemporary genomic methodologies.PLoS genetics · 2021Article
- The Intersection of the Genetic Architectures of Orofacial Clefts and Normal Facial Variation.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
As technology advances and collaborations grow, our ability to finely quantify and explore morphological variation in 3D structures can enable important discoveries and insights into clinical, evolutionary, and genetic questions. However, it is critical to explore and understand the relative contribution of potential sources of error to the structures under study. In this study, we isolated the level of error in 3D facial images attributable to four sources, using the 3dMDface and Vectra H1 camera systems. When the two camera systems are used separately to image human participants, this analysis finds an upper bound of error potentially introduced by the use of the 3dMDface or Vectra H1 camera systems, in conjunction with the MeshMonk registration toolbox, at 0.44 mm and 0.40 mm, respectively. For studies using both camera systems, this upper bound increases to 0.85 mm, on average, and there are systematic differences in the representation of the eyelids, nostrils, and mouth by the two camera systems. Our results highlight the need for careful assessment of potential sources of error in 3D images, both in terms of magnitude and position, especially when dealing with very small measurements or performing many tests.
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Registered trials
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.