ArticleThe Angle orthodontist2020
Automated identification of cephalometric landmarks:
Article in The Angle orthodontist, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 118 papers, 2 of them syntheses that pooled it.
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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
118 citing papers in PubMed, 2 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
- Deep learning for cephalometric landmark detection: systematic review and meta-analysis.Clinical oral investigations · 2021Pooled it
- Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane.European journal of medical research · 2024Trial
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Automated YOLO-Based Cephalometric Landmark Detection for ANB-Based Skeletal Classification: A Retrospective Single-Centre Study.Journal of clinical medicine · 2026Article
- Agreement of WebCeph-Based Automated and Expert-Adjusted Cephalometric Analyses with Manual and Dolphin Tracings.Diagnostics (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
- Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.Progress in orthodontics · 2026Article
- A standardized and efficient intensity-based image registration framework for quantitative cranial base superimposition.Biomedical engineering online · 2026Article
- Comparison of W. Arnett's cephalometric analysis measurements performed using artificial intelligence and manual measurements.Clinical oral investigations · 2026Article
- The Role of Artificial Intelligence in Orthognathic Surgery: A Scoping Review.Dentistry journal · 2026Review
- Comparison of multistage and single-stage framework for automated landmark localization and radiologic measurement: a case of the C1-2 complex on cervical spine lateral radiographs.BMC medical imaging · 2026Article
- Article
- Evaluation of cephalometric landmarks using artificial intelligence compared to the manual method.Head & face medicine · 2026Article
- Artificial Intelligence Methods in Cephalometric Image Analysis-A Systematic Narrative Review.Journal of clinical medicine · 2026Review
- 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
- 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
- Evaluation of orthognathic surgery planning with artificial intelligence: a prospective, comparative study.European oral research · 2026Article
58 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare detection patterns of 80 cephalometric landmarks identified by an automated identification system (AI) based on a recently proposed deep-learning method, the You-Only-Look-Once version 3 (YOLOv3), with those identified by human examiners. MATERIALS AND
methodsThe YOLOv3 algorithm was implemented with custom modifications and trained on 1028 cephalograms. A total of 80 landmarks comprising two vertical reference points and 46 hard tissue and 32 soft tissue landmarks were identified. On the 283 test images, the same 80 landmarks were identified by AI and human examiners twice. Statistical analyses were conducted to detect whether any significant differences between AI and human examiners existed. Influence of image factors on those differences was also investigated.
resultsUpon repeated trials, AI always detected identical positions on each landmark, while the human intraexaminer variability of repeated manual detections demonstrated a detection error of 0.97 ± 1.03 mm. The mean detection error between AI and human was 1.46 ± 2.97 mm. The mean difference between human examiners was 1.50 ± 1.48 mm. In general, comparisons in the detection errors between AI and human examiners were less than 0.9 mm, which did not seem to be clinically significant.
conclusionsAI showed as accurate an identification of cephalometric landmarks as did human examiners. AI might be a viable option for repeatedly identifying multiple cephalometric landmarks.
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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.