ArticleThe Angle orthodontist2021
Evaluation of automated cephalometric analysis based on the latest deep learning method.
Article in The Angle orthodontist, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 5 of them syntheses that pooled it.
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Who cites it
62 citing papers in PubMed, 5 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
- Performance of artificial intelligence on cervical vertebral maturation assessment: 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
- Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis.La Radiologia medica · 2023Pooled it
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Artificial-Intelligence-Based Cephalometric Landmark Detection in Lateral Cephalograms.Journal of clinical medicine · 2026Article
- Reliability-Aware View-Adaptive Consensus for 3D Cephalometric Landmark Identification.Journal of imaging informatics in medicine · 2026Article
- Left-Right Consistency and Quantification of Craniofacial Asymmetry by Two Fully Automatic AI-Powered Three-Dimensional Cephalometry Systems on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- Gonial Angle Estimation on Dental Panoramic X-Rays Using Deep Learning: A Transfer Learning Approach for Keypoint Detection.Journal of imaging informatics in medicine · 2026Article
- Evaluation of artificial intelligence-based cephalometric analysis compared to the manual method.Progress in orthodontics · 2026Article
- Clinical Reliability of AI-Based Cephalometric Analysis Using WebCeph: A Comparative Agreement Study.Journal of clinical medicine · 2026Article
- 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
- Evaluation of cephalometric landmarks using artificial intelligence compared to the manual method.Head & face medicine · 2026Article
- Deep learning for Angle classification based on intraoral photographs: an interpretability perspective.BMC oral health · 2026Article
- Evaluating a cost-effective, web-based AI platform for lateral cephalometric analysis: a comparative in-silico study.BMC oral health · 2025Article
- Applications of artificial intelligence in diagnosis and treatment planning of orthodontics: a narrative review.The Saudi dental journal · 2025Review
- What amount of data is required to develop artificial intelligence that can accurately predict soft tissue changes after orthognathic surgery?The Angle orthodontist · 2025Article
- Influence of head positioning errors on the accuracy of fully automated artificial intelligence-based cephalometric software.The Angle orthodontist · 2025Article
- Real-time segmentation and detection of ponticulus posticus in lateral cephalometric radiographs using YOLOv8: a step towards enhanced clinical evaluation.BMC oral health · 2025Article
2 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
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Abstract
objectivesTo compare an automated cephalometric analysis based on the latest deep learning method of automatically identifying cephalometric landmarks (AI) with previously published AI according to the test style of the worldwide AI challenges at the International Symposium on Biomedical Imaging conferences held by the Institute of Electrical and Electronics Engineers (IEEE ISBI). MATERIALS AND
methodsThis latest AI was developed by using a total of 1983 cephalograms as training data. In the training procedures, a modification of a contemporary deep learning method, YOLO version 3 algorithm, was applied. Test data consisted of 200 cephalograms. To follow the same test style of the AI challenges at IEEE ISBI, a human examiner manually identified the IEEE ISBI-designated 19 cephalometric landmarks, both in training and test data sets, which were used as references for comparison. Then, the latest AI and another human examiner independently detected the same landmarks in the test data set. The test results were compared by the measures that appeared at IEEE ISBI: the success detection rate (SDR) and the success classification rates (SCR).
resultsSDR of the latest AI in the 2-mm range was 75.5% and SCR was 81.5%. These were greater than any other previous AIs. Compared to the human examiners, AI showed a superior success classification rate in some cephalometric analysis measures.
conclusionsThis latest AI seems to have superior performance compared to previous AI methods. It also seems to demonstrate cephalometric analysis comparable to human examiners.
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