SynthesisClinical oral investigations2023
Tooth automatic segmentation from CBCT images: a systematic review.
Synthesis in Clinical oral investigations, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Comparisons of artificial intelligence automated segmentation techniques to manual segmentation techniques of the maxilla and maxillary sinus for CT or cone-beam CT scans-a systematic review.Dento maxillo facial radiology · 2025Pooled it
- Evolution of deep learning tooth segmentation from CT/CBCT images: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Automatic multimodal registration of cone-beam computed tomography and intraoral scans: a systematic review and meta-analysis.Clinical oral investigations · 2025Pooled it
- Capabilities of Cephalometric Methods to Study X-rays in Three-Dimensional Space (Review).Sovremennye tekhnologii v meditsine · 2024Pooled it
- A narrative review of artificial intelligence in dental imaging: from dataset design to clinical translation.Oral radiology · 2026Review
- Review
- Acceptability, deviation, and efficiency of automated artificial intelligence-based implant planning methods: A comparison with human intelligence.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- Evaluating Safety and Anatomical Eligibility for Paranasal Implants in the Atrophic Maxilla: A Segmentation-Assisted Proof-of-Concept Study.Journal of clinical medicine · 2026Article
- LADAS: A Localization-Adaptive Dual-Branch Framework for Accurate Delineation of Teeth and Jaw Bone in Axial CBCT Slices.Journal of imaging informatics in medicine · 2026Article
- Orthodontists' Perceptions, Attitudes, and Adoption of Artificial Intelligence: A Scoping Review.Dentistry journal · 2026Review
- Root canal segmentation from cone-beam computed tomography guided by micro-computed tomography based on deep learning.BMC oral health · 2026Article
- Fully automatic AI diagnosis of alveolar bone defect in anterior teeth on CBCT images.Clinical oral investigations · 2026Article
- Artificial intelligence-enabled automatic segmentation of impacted mandibular third molars: A comprehensive comparison of multiple algorithms.Journal of dental sciences · 2026Article
- Deep learning-based 3D automatic segmentation of impacted canines in CBCT scans.BMC oral health · 2025Article
- [Study on dental image segmentation and automatic root canal measurement based on multi-stage deep learning using cone beam computed tomography].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Influence of CBCT device, voxel size, and segmentation software on the accuracy of tooth volume measurements.BMC oral health · 2025Article
- Automated Cone Beam Computed Tomography Segmentation of Multiple Impacted Teeth With or Without Association to Rare Diseases: Evaluation of Four Deep Learning-Based Methods.Orthodontics & craniofacial research · 2025Article
- Applications of artificial intelligence in orthodontics: a bibliometric and visual analysis.Clinical oral investigations · 2025Review
- Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.International journal of paediatric dentistry · 2025Article
- Deep ensemble learning-driven fully automated multi-structure segmentation for precision craniomaxillofacial surgery.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo describe the current state of the art regarding technological advances in full-automatic tooth segmentation approaches from 3D cone-beam computed tomography (CBCT) images. MATERIALS AND
methodsIn March 2023, a search strategy without a timeline setting was carried out through a combination of MeSH terms and free text words pooled through Boolean operators ('AND', 'OR') on the following databases: PubMed, Scopus, Web of Science and IEEE Explore. Randomized and non-randomized controlled trials, cohort, case-control, cross-sectional and retrospective studies in the English language only were included.
resultsThe search strategy identified 541 articles, of which 23 have been selected. The most employed segmentation methods were based on deep learning approaches. One article exposed an automatic approach for tooth segmentation based on a watershed algorithm and another article used an improved level set method. Four studies presented classical machine learning and thresholding approaches. The most employed metric for evaluating segmentation performance was the Dice similarity index which ranged from 90 ± 3% to 97.9 ± 1.5%.
conclusionsThresholding appeared not reliable for tooth segmentation from CBCT images, whereas convolutional neural networks (CNNs) have been demonstrated as the most promising approach. CNNs could help overcome tooth segmentation's main limitations from CBCT images related to root anatomy, heavy scattering, immature teeth, metal artifacts and time consumption. New studies with uniform protocols and evaluation metrics with random sampling and blinding for data analysis are encouraged to objectively compare the different deep learning architectures' reliability. CLINICAL RELEVANCE: Automatic tooth segmentation's best performance has been obtained through CNNs for the different ambits of digital dentistry.
Indexed as
Identifiers
37148371What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.