Dataset Information

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 important for the CNNs.

Results

The CNN of ResNet18 demonstrated the best performance [accuracy =0.82, 95% confidence interval (CI): 0.80-0.84; precision =0.81, 95% CI: 0.73-0.89; sensitivity =0.85, 95% CI: 0.79-0.91; specificity =0.82, 95% CI: 0.76-0.88; and AUC =0.89, 95% CI: 0.86-0.92], compared with VGG19 and Inception V3 as well as the comparator dentists. Therefore, ResNet18 was chosen to integrate with clinical parameters to produce the multi-modal CNN of ResNet18 + C, which showed a significantly enhanced performance (accuracy =0.86, 95% CI: 0.84-0.88; precision =0.85, 95% CI: 0.76-0.94; sensitivity =0.89, 95% CI: 0.83-0.95; specificity =0.86, 95% CI: 0.79-0.93; and AUC =0.94, 95% CI: 0.91-0.97).

Conclusions

The CNN of ResNet18 showed good performance (accuracy, precision, sensitivity, specificity, and AUC) for the diagnosis of deep caries and pulpitis. The multi-modal CNN of ResNet18 + C (ResNet18 integrated with clinical parameters) demonstrated a significantly enhanced performance, with promising potential for the diagnosis of deep caries and pulpitis.

SUBMITTER: Zheng L

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

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Zheng Liwen L Wang Haolin H Mei Li L Chen Qiuman Q Zhang Yuxin Y Zhang Hongmei H

Annals of translational medicine 20210501 9

<h4>Background</h4>An accurate diagnosis of deep caries and pulpitis on periapical radiographs is a clinical challenge.<h4>Methods</h4>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 CNN ...[more]

PMID: 34268376

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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