Observational studyClinical oral investigations2022
Evaluation of different registration methods and dental restorations on the registration duration and accuracy of cone beam computed tomography data and intraoral scans: a retrospective clinical study.
Observational study in Clinical oral investigations, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
What it found
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- Automatic multimodal registration of cone-beam computed tomography and intraoral scans: a systematic review and meta-analysis.Clinical oral investigations · 2025Pooled it
- Fully automated deep learning for the identification and classification of dental implant brands from multimodal radiographic images.Clinical oral investigations · 2026Article
- Tridimensional Analysis of Maxillary Teeth Movements in Premolar-Extraction Cases Treated With Clear Aligners.International dental journal · 2026Article
- Integrated correlation analysis of the thickness of buccal bone and gingiva of maxillary incisors.Journal of applied oral science : revista FOB · 2024Article
- Novel Procedure for Automatic Registration between Cone-Beam Computed Tomography and Intraoral Scan Data Supported with 3D Segmentation.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
objectivesTo evaluate whether the accuracy and duration of registration for cone beam computed tomography (CBCT) and intraoral scans differ according to the method of registration and ratio of dental restorations to natural teeth. MATERIALS AND
methodsCBCT data and intraoral scans of eligible patients were grouped as follows according to the ratio of the number of dental restorations to the number of natural teeth (N): group 1, N = 0%; group 2, 0% < N < 50%; group 3, 50% ≤ N < 100%; and group 4, 100% ≤ N. Marker-free registration was performed with a deep learning-based platform and four implant planning software with different registration methods (two point-based, one surface-based, and one manual registration software) by a single operator, and the time consumption was recorded. Registration accuracy was evaluated by measuring the distances between the three-dimensional models of CBCT data and intraoral scans.
resultsA total of 36 patients, one jaw per patient, were enrolled. Although registration accuracy was similar, the time consumed for registration significantly differed for the different methods. The deep learning-based registration method consumed the least time. Greater proportions of dental restorations significantly reduced the registration accuracy for semi-automatic and deep learning-based methods and reduced the time consumed for semi-automatic registration.
conclusionsNo superiority in registration accuracy was found. The proportion of dental restorations significantly affects the accuracy and duration of registration for CBCT data and intraoral scans. CLINICAL
trial registrationClinicalTrials.gov Identifier: KCT0006710 CLINICAL RELEVANCE: Registration accuracy for virtual implant planning decreases when the proportion of dental restorations increases regardless of registration methods.
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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.