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BMC Oral Health
Published in: BMC Oral Health 1/2023

Open Access 01-12-2023 | Artificial Intelligence | Research

Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study

Authors: Junhua Zhu, Zhi Chen, Jing Zhao, Yueyuan Yu, Xiaojuan Li, Kangjian Shi, Fan Zhang, Feifei Yu, Keying Shi, Zhe Sun, Nengjie Lin, Yuanna Zheng

Published in: BMC Oral Health | Issue 1/2023

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Abstract

Background

Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.

Methods

The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden’s index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).

Results

Sensitivity, specificity, and Youden’s index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976–0.983, impacted teeth), 0.975 (95%CI: 0.972–0.978, full crowns), and 0.935 (95%CI: 0.929–0.940, residual roots), 0.939 (95%CI: 0.934–0.944, missing teeth), and 0.772 (95%CI: 0.764–0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).

Conclusions

The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3–10 years of experience. However, the AI framework for caries diagnosis should be improved.
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Metadata
Title
Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study
Authors
Junhua Zhu
Zhi Chen
Jing Zhao
Yueyuan Yu
Xiaojuan Li
Kangjian Shi
Fan Zhang
Feifei Yu
Keying Shi
Zhe Sun
Nengjie Lin
Yuanna Zheng
Publication date
01-12-2023
Publisher
BioMed Central
Published in
BMC Oral Health / Issue 1/2023
Electronic ISSN: 1472-6831
DOI
https://doi.org/10.1186/s12903-023-03027-6

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