ArticleBMC oral health2025
Automatic identification of hard and soft tissue landmarks in cone-beam computed tomography via deep learning with diversity datasets: a methodological study.
Article in BMC oral health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical Equivalence of a CNN-Based Automated Soft Tissue Landmark Detection System on 2D Facial Images.Diagnostics (Basel, Switzerland) · 2026Article
- Clinical accuracy of cephalometric analysis using deep learning-based automated landmark identification on CBCT in class I and class II malocclusions.Scientific reports · 2026Article
- Automatic identification of anatomical landmarks in three-dimensional computed tomography/cone-beam computed tomography: a scoping review.Frontiers in dental medicine · 2026Review
- Attention-Base deep learning for 3D craniofacial soft tissue landmark detection and diagnosis in orthodontics.Scientific reports · 2025Article
- Automated landmark-based mid-sagittal plane: reliability for 3-dimensional mandibular asymmetry assessment on head CT scans.Clinical oral investigations · 2025Article
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Authors and funding
10 authors.
Funding
Abstract
backgroundManual landmark detection in cone beam computed tomography (CBCT) for evaluating craniofacial structures relies on medical expertise and is time-consuming. This study aimed to apply a new deep learning method to predict and locate soft and hard tissue craniofacial landmarks on CBCT in patients with various types of malocclusion.
methodsA total of 498 CBCT images were collected. Following the calibration procedure, two experienced clinicians identified 43 landmarks in the x-, y-, and z-coordinate planes on the CBCT images using Checkpoint Software, creating the ground truth by averaging the landmark coordinates. To evaluate the accuracy of our algorithm, we determined the mean absolute error along the x-, y-, and z-axes and calculated the mean radial error (MRE) between the reference landmark and predicted landmark, as well as the successful detection rate (SDR).
resultsEach landmark prediction took approximately 4.2 s on a conventional graphics processing unit. The mean absolute error across all coordinates was 0.74 mm. The overall MRE for the 43 landmarks was 1.76 ± 1.13 mm, and the SDR was 60.16%, 91.05%, and 97.58% within 2-, 3-, and 4-mm error ranges of manual marking, respectively. The average MRE of the hard tissue landmarks (32/43) was 1.73 mm, while that for soft tissue landmarks (11/43) was 1.84 mm.
conclusionsOur proposed algorithm demonstrates a clinically acceptable level of accuracy and robustness for automatic detection of CBCT soft- and hard-tissue landmarks across all types of malformations. The potential for artificial intelligence to assist in identifying three dimensional-CT landmarks in routine clinical practice and analysing large datasets for future research is promising.
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