ArticleInternational journal of paediatric dentistry2025
Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.
Article in International journal of paediatric dentistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- FDA-Approved AI Solutions in Dental Imaging: A Narrative Review of Applications, Evidence, and Outlook.International dental journal · 2026Review
- Comparison of AI-Powered Tools for CBCT-Based Mandibular Incisive Canal Segmentation: A Validation Study.Clinical oral implants research · 2025Article
- Unveiling the power of artificial intelligence for image-based diagnosis and treatment in endodontics: An ally or adversary?International endodontic journal · 2025Review
- Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.International journal of paediatric dentistry · 2025Article
- Comparative evaluation of CNN architectures for detection of dental pulp calcifications on CBCT slices.Science progressArticle
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Authors and funding
5 authors.
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Abstract
backgroundPrimary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise.
aimThe aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS).
designA dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them.
resultsAS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively).
conclusionThe platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.
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