ArticleScientific data2025
A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- 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
- Cross-Domain Generalization of Deep Learning Architectures for Cephalometric Landmark Detection: A Dual-Dataset and Multi-Device Benchmark.Diagnostics (Basel, Switzerland) · 2026Article
- Automated YOLO-Based Cephalometric Landmark Detection for ANB-Based Skeletal Classification: A Retrospective Single-Centre Study.Journal of clinical medicine · 2026Article
- Single-model deep learning approach for simultaneous cervical vertebral maturation staging and skeletal jaw relationship on lateral cephalograms using YOLOv8 and CNN.BMC oral health · 2026Article
- Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.Diagnostics (Basel, Switzerland) · 2026Article
- A Novel Multimodal Deep Image Analysis Model for Predicting Extraction/Non-Extraction Decision.Orthodontics & craniofacial research · 2026Article
- BRAR-anchored multimodal dataset of panoramic radiographs for periodontal bone resorption grading.Scientific data · 2025Article
- Automated assessment of pelvic radiographs using deep learning: A reliable diagnostic tool for pelvic malalignment.Heliyon · 2024Article
- Three-Dimensional Craniofacial Landmark Detection in Series of CT Slices Using Multi-Phased Regression Networks.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
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Abstract
Accurate identification and localization of cephalometric landmarks are crucial for diagnosing and quantifying anatomical abnormalities in orthodontics. Traditional manual annotation of these landmarks on lateral cephalograms (LCRs) is time-consuming and subject to inter- and intra-expert variability. Attempts to develop automated landmark detection systems have persistently been made; however, they are inadequate for orthodontic applications due to the unavailability of a diverse dataset. In this work, we introduce a state-of-the-art cephalometric dataset designed to advance AI-driven quantitative morphometric analysis. Our dataset comprises 1,000 LCRs acquired from seven different imaging devices with varying resolutions, making it the most diverse and comprehensive collection to date. Each radiograph is meticulously annotated by clinical experts with 29 cephalometric landmarks, including the most extensive set of dental and soft tissue markers ever included in a public dataset. Additionally, we provide cervical vertebral maturation (CVM) stage annotations, marking the first standard resource for CVM classification. We anticipate that this dataset will serve as a benchmark for developing robust, automated landmark detection frameworks, with applications extending beyond orthodontics.
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