Document Type
Article
Publication Date
12-2023
Publisher
Elsevier
Source Publication
Journal of Dentistry
Source ISSN
0300-5712
Original Item ID
DOI: 10.1016/j.jdent.2023.104768
Abstract
Objectives
The purpose of this study was to utilize a no-code computer vision platform to develop, train, and evaluate a model specifically designed for segmenting dental restorations on panoramic radiographs.
Methods
One hundred anonymized panoramic radiographs were selected for this study. Accurate labeling of dental restorations was performed by calibrated dental faculty and students, with subsequent final review by an oral radiologist. The radiographs were automatically split within the platform into training (70 %), development (20 %), and testing (10 %) subgroups. The model was trained for 40 epochs using a medium model size. Data augmentation techniques available within the platform, namely horizontal and vertical flip, were utilized on the training set to improve the model's predictions. Post-training, the model was tested for independent predictions. The model's diagnostic validity was assessed through the calculation of sensitivity, specificity, accuracy, precision, F1-score by pixel and by tooth, and by ROC-AUC.
Results
A total of 1,108 restorations were labeled on 960 teeth. At a confidence threshold of 0.95, the model achieved 86.64 % sensitivity, 99.78 % specificity, 99.63 % accuracy, 82.4 % precision and an F1-score of 0.844 by pixel. The model achieved 98.34 % sensitivity, 98.13 % specificity, 98.21 % accuracy, 98.85 % precision and an F1-score of 0.98 by tooth. ROC curve showed high performance with an AUC of 0.978.
Conclusions
The no-code computer vision platform used in this study accurately detected dental restorations on panoramic radiographs. However, further research and validation are required to evaluate the performance of no-code platforms on larger and more diverse datasets, as well as for other detection and segmentation tasks.
Clinical significance
The advent of no-code computer vision holds significant promise in dentistry and dental research by eliminating the requirement for coding skills, democratizing access to artificial intelligence tools, and potentially revolutionizing dental diagnostics.
Recommended Citation
Hamdan, Manal; Badr, Zaid; Bjork, Jennifer; Saxe, Reagan; Malensek, Francesca; Miller, Caroline; Shah, Rakhi; Han, Shengtong; and Mohammad-Rahimi, Hossein, "Detection of Dental Restorations Using No-Code Artificial Intelligence" (2023). School of Dentistry Faculty Research and Publications. 562.
https://epublications.marquette.edu/dentistry_fac/562
Comments
Accepted version. Journal of Dentistry, Vol. 139 (December 2023). DOI. © 2023 Elsevier. Used with permission.