Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.

BACKGROUND

This study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.

METHODS

This 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).

RESULTS

The 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.

CONCLUSIONS

The 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).