Ensuring Integrity in Dental Education: Developing a Novel AI Model for Consistent and Traceable Image Analysis in Preclinical Endodontic Procedures

Authors

• Mohamed Ibrahim, Marquette UniversityFollow
• Meisam Omidi, Marquette University
• Arndt Guentsch, Marquette University
• Joseph Gaffney, Marquette University
• Jennifer Talley, Marquette University

Document Type

Article

Publication Date

6-2025

Publisher

Wiley

Source Publication

International Endodontic Journal

Source ISSN

0143-2885

Original Item ID

DOI: 10.1111/iej.14273

Abstract

Aim

Academic integrity is crucial in dental education, especially during practical exams assessing competencies. Traditional oversight may not detect sophisticated academic dishonesty methods like radiograph substitution or tampering. This study aimed to develop and evaluate a novel artificial intelligence (AI) model utilizing a Siamese neural network to detect inconsistencies in radiographic images taken for root canal treatment (RCT) procedures in preclinical endodontic courses, thereby enhancing educational integrity.

Methododology

A Siamese neural network was designed to compare radiographs from different RCT procedures. The model was trained on 3390 radiographs, with data augmentation applied to improve generalizability. The dataset was split into training, validation, and testing subsets. Performance metrics included accuracy, precision, sensitivity (recall), and F1-score. Cross-validation and hyperparameter tuning optimized the model.

Results

Our AI model achieved an accuracy of 89.31%, a precision of 76.82%, a sensitivity of 84.82%, and an F1-score of 80.50%. The optimal similarity threshold was 0.48, where maximum accuracy was observed. The confusion matrix indicated a high rate of correct classifications, and cross-validation confirmed the model's robustness with a standard deviation of 1.95% across folds.

Conclusions

The AI-driven Siamese neural network effectively detects radiographic inconsistencies in RCT preclinical procedures. Implementing this novel model will serve as an objective tool to uphold academic integrity in dental education, enhance the fairness and reliability of assessments, promote a culture of honesty amongst students, and reduce the administrative burden on educators.

Comments

International Endodontic Journal, Vol. 59, No. 6 (June 2025): 1122-1131. DOI.

Recommended Citation

Ibrahim, Mohamed; Omidi, Meisam; Guentsch, Arndt; Gaffney, Joseph; and Talley, Jennifer, "Ensuring Integrity in Dental Education: Developing a Novel AI Model for Consistent and Traceable Image Analysis in Preclinical Endodontic Procedures" (2025). School of Dentistry Faculty Research and Publications. 619.
https://epublications.marquette.edu/dentistry_fac/619