ArticleDento maxillo facial radiology2023
Age estimation based on 3D pulp segmentation of first molars from CBCT images using U-Net.
Article in Dento maxillo facial radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Volumetric Pulp Analysis of Mandibular Molars on CBCT as a Supplementary Indicator for Age-Related Dental Changes: A Preliminary Investigation in a Turkish Population Sample.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-view deep fusion for forensic dental age estimation in tomographic imaging.International journal of legal medicine · 2026Article
- CBCT-based volumetric analysis of mandibular central incisors for age estimation in children and adolescents: a pilot study.Scientific reports · 2026Article
- Article
- Three-dimensional automatic segmentation of root canals with focus on the second mesiobuccal canal using nnU-Netv2 on CBCT images: deep learning approach.BMC oral health · 2026Article
- Deep learning-based method for estimating age from periapical radiographs of upper incisors in a Thai population.Imaging science in dentistry · 2025Article
- Assessment of volumetric changes after regenerative endodontic procedures using semiautomated and 3D U-NET automated CBCT segmentation: a retrospective cohort study.BMC oral health · 2025Article
- Two step approach for detecting and segmenting the second mesiobuccal canal of maxillary first molars on cone beam computed tomography (CBCT) images via artificial intelligence.BMC oral health · 2025Observational
- Age estimation from morphometric features of maxillary central incisors using CBCT images.BMC oral health · 2025Article
- Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?International endodontic journal · 2025Review
- Optimal Training Positive Sample Size Determination for Deep Learning with a Validation on CBCT Image Caries Recognition.Diagnostics (Basel, Switzerland) · 2024Article
- A reliable deep-learning-based method for alveolar bone quantification using a murine model of periodontitis and micro-computed tomography imaging.Journal of dentistry · 2024Article
- Machine learning assessment of dental age classification based on cone-beam CT images: a different approach.Dento maxillo facial radiology · 2024Article
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5 authors.
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Abstract
objectiveTo train a U-Net model to segment the intact pulp cavity of first molars and establish a reliable mathematical model for age estimation.
methodsWe trained a U-Net model by 20 sets of cone-beam CT images and this model was able to segment the intact pulp cavity of first molars. Utilizing this model, 239 maxillary first molars and 234 mandibular first molars from 142 males and 135 females aged 15-69 years old were segmented and the intact pulp cavity volumes were calculated, followed by logarithmic regression analysis to establish the mathematical model with age as the dependent variable and pulp cavity volume as the independent variable. Another 256 first molars were collected to estimate ages with the established model. Mean absolute error and root mean square error between the actual and the estimated ages were used to assess the precision and accuracy of the model.
resultsThe dice similarity coefficient of the U-Net model was 95.6%. The established age estimation model was [Formula: see text] (
conclusionThe trained U-Net model can accurately segment pulp cavity of the first molars from three-dimensional cone-beam CT images. The segmented pulp cavity volumes could be used to estimate the human ages with reasonable precision and accuracy.
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