ArticleThe Angle orthodontist2019
Automated identification of cephalometric landmarks:
Article in The Angle orthodontist, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 4 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.
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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
116 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.BMC oral health · 2025Pooled it
- Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.European journal of orthodontics · 2024Pooled it
- Capabilities of Cephalometric Methods to Study X-rays in Three-Dimensional Space (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- Deep learning for cephalometric landmark detection: systematic review and meta-analysis.Clinical oral investigations · 2021Pooled it
- Bibliometric Mapping of Digitalization in Orthodontics: Evaluating Scientific Contributions, Collaboration Networks, and Future Directions.Turkish journal of orthodontics · 2026Article
- Diagnostic Accuracy of a Machine Learning Model for Cervical Vertebra-Based Skeletal Maturity Assessment in Pediatric and Adolescent Patients Using Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial-Intelligence-Based Cephalometric Landmark Detection in Lateral Cephalograms.Journal of clinical medicine · 2026Article
- Automated YOLO-Based Cephalometric Landmark Detection for ANB-Based Skeletal Classification: A Retrospective Single-Centre Study.Journal of clinical medicine · 2026Article
- Deep Learning-Based Evaluation of Maxillary Dental Midline Deviation on Orthodontic Frontal Photographs.Bioengineering (Basel, Switzerland) · 2026Article
- Comparison of linear and angular cephalometric measurements in CBCT using semi-automated software and artificial intelligence-generated cephalograms.BMC oral health · 2026Article
- The Role of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.Dentistry journal · 2026Review
- Evaluation of Accuracy and Reproducibility of an AI-Based Cephalometric Landmark Detection Software: A Comparative Study with Manual Annotations.Journal of clinical and experimental dentistry · 2026Review
- Comparative performance analysis of AI-based large language models in assessing cervical vertebral maturation stages on lateral cephalometric radiographs.BMC oral health · 2026Article
- Automated deep learning detection of orthodontically induced external apical root resorption in maxillary incisors on panoramic radiographs.Progress in orthodontics · 2026Article
- Oral radiologists and orthodontists' attitude regarding the use of artificial intelligence for cephalometric analysis.Journal of orofacial orthopedics = Fortschritte der Kieferorthopadie : Organ/official journal Deutsche Gesellschaft fur Kieferorthopadie · 2026Article
- Application of artificial intelligence in oral health management: challenges and opportunities.Frontiers in medicine · 2026Review
- Assessment of AI-based cephalometric landmark recognition on lateral cephalometric radiographs.Frontiers in oral health · 2026Article
- Clinical decisions in Orthodontics using x-ray-based images and artificial intelligence approaches: a scoping review.Dental press journal of orthodontics · 2026Article
- Evaluating a cost-effective, web-based AI platform for lateral cephalometric analysis: a comparative in-silico study.BMC oral health · 2025Article
- Comparative Evaluation of Craniofacial Growth Patterns Using AI-Driven Longitudinal Cephalometric Superimpositions in Adolescents.Journal of pharmacy & bioallied sciences · 2025Article
56 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare the accuracy and computational efficiency of two of the latest deep-learning algorithms for automatic identification of cephalometric landmarks. MATERIALS AND
methodsA total of 1028 cephalometric radiographic images were selected as learning data that trained You-Only-Look-Once version 3 (YOLOv3) and Single Shot Multibox Detector (SSD) methods. The number of target labeling was 80 landmarks. After the deep-learning process, the algorithms were tested using a new test data set composed of 283 images. Accuracy was determined by measuring the point-to-point error and success detection rate and was visualized by drawing scattergrams. The computational time of both algorithms was also recorded.
resultsThe YOLOv3 algorithm outperformed SSD in accuracy for 38 of 80 landmarks. The other 42 of 80 landmarks did not show a statistically significant difference between YOLOv3 and SSD. Error plots of YOLOv3 showed not only a smaller error range but also a more isotropic tendency. The mean computational time spent per image was 0.05 seconds and 2.89 seconds for YOLOv3 and SSD, respectively. YOLOv3 showed approximately 5% higher accuracy compared with the top benchmarks in the literature.
conclusionsBetween the two latest deep-learning methods applied, YOLOv3 seemed to be more promising as a fully automated cephalometric landmark identification system for use in clinical practice.
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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.