Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images

The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 p...

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Bibliographic Details
Published inInternational journal of intelligent systems Vol. 2023; pp. 1 - 8
Main Authors Syed, Ali Zakir, Çelik Ozen, Duygu, Abdelkarim, Ahmed Z., Duman, Şuayip Burak, Bayrakdar, İbrahim Şevki, Duman, Sacide, Celik, Özer, Orhan, Kaan
Format Journal Article
LanguageEnglish
Published New York Hindawi 23.10.2023
Hindawi Limited
Subjects
Archives & records
Artificial intelligence
Artificial neural networks
Automation
Business metrics
Computed tomography
Datasets
Deep learning
Diagnosis
Image segmentation
Intelligent systems
Labeling
Machine learning
Medical imaging
Morphology
Neural networks
Performance evaluation
Software
Teeth
Tomography
United States > US
Texas
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Summary:The detection of mesiodens supernumerary teeth is crucial for appropriate diagnosis and treatment. The study aimed to develop a convolutional neural network (CNN)-based model to automatically detect mesiodens in cone-beam computed tomography images. A datatest of anonymized 851 axial slices of 106 patients’ cone-beam images was used to process the artificial intelligence system for the detection and segmentation of mesiodens. The CNN model achieved high performance in mesiodens segmentation with sensitivity, precision, and F1 scores of 1, 0.9072, and 0.9513, respectively. The area under the curve (AUC) was 0.9147, indicating the model’s robustness. The proposed model showed promising potential for the automated detection of mesiodens, providing valuable assistance to dentists in accurate diagnosis.
ISSN:0884-8173
1098-111X
DOI:10.1155/2023/4415970