Significance of Endodontic Case Difficulty Assessment: A Retrospective Study

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).

Cross-Sectional Survey to Assess Dental Students' Perception of the Utilization of a Case Difficulty Assessment Form during Various Stages of Root Canal Therapy

Background

Several endodontic difficulty assessment forms are available to help dental students and general dentists estimate the difficulty of the case before initiating the endodontic treatment.

Objectives

This study aimed to assess if the American Association of Endodontics (AAE) case difficulty assessment form affects the dental student's perception of the difficulty encountered while performing root canal treatment (RCT).

Materials and Methods

This was a cross-sectional online survey. After obtaining IRB approval, an electronic questionnaire was sent to dental students enrolled in the 4th and 5th years at King Saud University (KSU) and King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Saudi Arabia. The questionnaire was divided into informed consent, demographic data, the use of the AAE case assessment form, RCT steps, diagnosis, local anesthesia administration, tooth isolation, and endodontic procedure.

Results

A total of 195 dental students participated in the study. There were 101 (52%) females, and 139 (71%) were from KSAU-HS. A positive association was found between students who used the AAE assessment form and who did not with their ability to reach the correct diagnosis (p=0.005), tooth isolation (p=0.03), and endodontic procedure difficulty score (p=0.018).

Conclusion

The use of the AAE form by dental students enabled them to differentiate between complicated and uncomplicated cases, thus lowering the difficulty encountered during RCT.

Artificial Intelligence in Endodontic Education

AIMS

The future dental and endodontic education must adapt to the current digitalized healthcare system in a hyper-connected world. The purpose of this scoping review was to investigate the ways an endodontic education curriculum could benefit from the implementation of artificial intelligence (AI) and overcome the limitations of this technology in the delivery of healthcare to patients.

METHODS

An electronic search was carried out up to December 2023 using MEDLINE, Web of Science, Cochrane Library, and a manual search of reference literature. Grey literature, ongoing clinical trials were also searched using ClinicalTrials.gov.

RESULTS

The search identified 251 records, of which 35 were deemed relevant to artificial intelligence (AI) and Endodontic education. Areas in which AI might aid students with their didactic and clinical endodontic education were identified as follows: 1) radiographic interpretation; 2) differential diagnosis; 3) treatment planning and decision-making; 4) case difficulty assessment; 5) preclinical training; 6) advanced clinical simulation and case-based training, 7) real-time clinical guidance; 8) autonomous systems and robotics; 9) progress evaluation and personalized education; 10) calibration and standardization.

CONCLUSIONS

AI in endodontic education will support clinical and didactic teaching through individualized feedback; enhanced, augmented, and virtually generated training aids; automated detection and diagnosis; treatment planning and decision support; and AI-based student progress evaluation, and personalized education. Its implementation will inarguably change the current concept of teaching Endodontics. Dental educators would benefit from introducing AI in clinical and didactic pedagogy; however, they must be aware of AI's limitations and challenges to overcome.

Clinical and medico-legal considerations in endodontics

Endodontic treatment can be challenging for a number of reasons, including the microscopic nature of the clinical environment, reliance on tactile sensation and lack of direct visualization of the work being performed. Commonly, endodontic patients present with pain and distress, which can exacerbate an already difficult clinical situation. Complications may might arise prior to, or during treatment, despite practising with the utmost care and skill. Preventing and managing these complications can take considerable time and energy, and oftentimes assistance from or referral to more experienced colleagues is required. The aim of this review is to discuss medico-legal considerations in endodontics, with clinical correlations and a focus on the Australian legal landscape. [Correction added on 18 October 2023, after first online publication: The abstract was amended from a structured to an unstructured abstract.].