Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks.

ABSTRACT:

Background

An accurate diagnosis of deep caries and pulpitis on periapical radiographs is a clinical challenge.

Methods

A total of 844 radiographs were included in this study. Of the 844, 717 (85%) were used for training and 127 (15%) were used for testing the three convolutional neural networks (CNNs) (VGG19, Inception V3, and ResNet18). The performance [accuracy, precision, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC)] of the CNNs were evaluated and compared. The CNN model with the best performance was further integrated with clinical parameters to see whether multi-modal CNN could provide an enhanced performance. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique illustrates what image feature was the most

SUBMITTER: Zheng L 

PROVIDER: S-EPMC8246233 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature