ArticleScientific reports2021
3D cephalometric landmark detection by multiple stage deep reinforcement learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled 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.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- Can classification strategies improve automated cervical vertebral maturation staging? A comparative study.Scientific reports · 2026Article
- Accuracy and reliability of 3D cephalometric landmark detection with deep learning.European journal of medical research · 2025Article
- Development of Artificial Intelligence-Supported Automatic Three-Dimensional Surface Cephalometry.Orthodontics & craniofacial research · 2025Article
- A User-Friendly Software for Automated Knowledge-Based Virtual Surgical Planning in Mandibular Reconstruction.Journal of clinical medicine · 2025Article
- Evaluating Virtual Planning Accuracy in Bimaxillary Advancement Surgery: A Retrospective Study Introducing the Planning Accuracy Coefficient.Journal of clinical medicine · 2025Article
- Article
- Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.Insights into imaging · 2024Article
- Comparison of Three Commercially Available, AI-Driven Cephalometric Analysis Tools in Orthodontics.Journal of clinical medicine · 2024Article
- Fully automated landmarking and facial segmentation on 3D photographs.Scientific reports · 2024Article
- Skeletal facial asymmetry: reliability of manual and artificial intelligence-driven analysis.Dento maxillo facial radiology · 2024Article
- Deep learning for 3D cephalometric landmarking with heterogeneous multi-center CBCT dataset.PloS one · 2024Article
- Correlation Analysis of Nasal Septum Deviation and Results of AI-Driven Automated 3D Cephalometric Analysis.Journal of clinical medicine · 2023Article
- Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives.Healthcare (Basel, Switzerland) · 2023Review
- Artificial Intelligence Systems Assisting in the Assessment of the Course and Retention of Orthodontic Treatment.Healthcare (Basel, Switzerland) · 2023Review
- Concordance and reproducibility in the location of reference points for a volumetric craniofacial analysis: Cross-sectional study.Journal of dental research, dental clinics, dental prospects · 2023Article
- Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network.Journal of personalized medicine · 2022Article
- Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy.Healthcare (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.
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.