ArticleBMC oral health2024
Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Pulp calcification and pulp canal obliteration: from biological mechanisms to contemporary clinical management and digital navigation.The Saudi dental journal · 2026Review
- Artificial intelligence in the detection of dental pulp calcifications: a scoping review.BMC oral health · 2026Article
- Comparative evaluation of CNN architectures for detection of dental pulp calcifications on CBCT slices.Science progressArticle
- Transfer learning models in the detection of pulp calcifications- A preliminary study.Journal of oral biology and craniofacial researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.
methodsThis study retrospectively analysed 150 CBCT images. Pulp calcification was identified and manually annotated by three endodontists with 10 years of experience. A DNN model based on the U-Net architecture was constructed to identify pulp calcification, and ten rounds of fourfold cross-validation were conducted. The model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).
resultsThe model achieved a sensitivity of 75.91 ± 2.84% in automatically identifying pulp calcification, with a specificity of 68.88 ± 2.35%, an accuracy of 72.78 ± 2.13%, and an AUC of 73.68 ± 3.09%. According to the ranking for diagnostic tests, the proposed method achieved a "good" grade for sensitivity, accuracy, and AUC and a "fair" grade for specificity.
conclusionsThe results indicate that the proposed method shows promise for identifying pulp calcification on CBCT images. Future research aims to expand the dataset and refine the model, thereby enhancing its clinical applicability. The integration of artificial intelligence into diagnostic and treatment systems is anticipated to increase the efficiency of diagnosing pulp calcification and assist dentists in assessing the difficulty of root canal treatment cases preoperatively. CLINICAL REGISTRATION: Registration was performed on the Chinese Clinical Trial Registry ( https://www.chictr.org.cn/ ) (Registration number: ChiCTR2300077078, 27/10/2023) and National Medical Research Registry Information System ( https://www.medicalresearch.org.cn/ , 30/10/2023) (Number: MR-44-23-039664).
Indexed as
Identifiers
What 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.