Classification of Caries in Third Molars on Panoramic Radiographs Using Deep Learning

Overview

Journal Sci Rep

Specialty Science

Date 2021 Jun 16

PMID 34131266

Citations 37

Authors

Shankeeth Vinayahalingam

Steven Kempers

Lorenzo Limon

Dionne Deibel

Thomas Maal

Marcel Hanisch

Stefaan Berge

Tong Xi

Affiliations

Soon will be listed here.

Abstract

The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

Citing Articles

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