Automated Mesiodens Detection with Deep-Learning-Based System Using Cone-Beam Computed Tomography Images
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Wiley-Hindawi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Classification, Teeth
Kaynak
International Journal of Intelligent Systems
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
2023
