ArticleScientific reports2024
Fully automated landmarking and facial segmentation on 3D photographs.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- 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
- Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.Diagnostics (Basel, Switzerland) · 2026Article
- Article
- Present and Future of the Use of Artificial Intelligence in Orthodontics.Bioengineering (Basel, Switzerland) · 2026Review
- VisionMetric Suite for periocular measurement in ophthalmic plastic surgery: a preliminary single-center validation study.Frontiers in medicine · 2026Article
- A personalized automated system of 3D facial soft tissue landmarks annotation based on deep learning and computer vision.BMC oral health · 2025Article
- Attention-Base deep learning for 3D craniofacial soft tissue landmark detection and diagnosis in orthodontics.Scientific reports · 2025Article
- Three-dimensional longitudinal assessment of nasal development with and without Nasoalveolar molding therapy in cleft lip and palate patients.Clinical oral investigations · 2025Article
- Facial Analysis for Plastic Surgery in the Era of Artificial Intelligence: A Comparative Evaluation of Multimodal Large Language Models.Journal of clinical medicine · 2025Article
- The accuracy of automated facial landmarking - a comparative study between Cliniface software and patch-based Convoluted Neural Network algorithm.European journal of orthodontics · 2025Article
- Article
- Accuracy Evaluation of a Three-Dimensional Face Reconstruction Model Based on the Hifi3D Face Model and Clinical Two-Dimensional Images.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach. Ten landmarks were manually annotated on 2897 3D facial photographs. The automated landmarking workflow involved two successive DiffusionNet models. The dataset was randomly divided into a training and test dataset. The precision of the workflow was evaluated by calculating the Euclidean distances between the automated and manual landmarks and compared to the intra-observer and inter-observer variability of manual annotation and a semi-automated landmarking method. The workflow was successful in 98.6% of all test cases. The deep learning-based landmarking method achieved precise and consistent landmark annotation. The mean precision of 1.69 ± 1.15 mm was comparable to the inter-observer variability (1.31 ± 0.91 mm) of manual annotation. Automated landmark annotation on 3D photographs was achieved with the DiffusionNet-based approach. The proposed method allows quantitative analysis of large datasets and may be used in diagnosis, follow-up, and virtual surgical planning.
Indexed as
Identifiers
What OpenQuestion holds
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.