期刊论文详细信息
BMC Oral Health | |
Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study | |
Research | |
Fan Zhang1  Xiaojuan Li1  Kangjian Shi1  Zhi Chen2  Jing Zhao2  Zhe Sun2  Nengjie Lin2  Junhua Zhu2  Keying Shi2  Feifei Yu2  Yuanna Zheng2  Yueyuan Yu2  | |
[1]College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China | |
[2]School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China | |
关键词: Artificial intelligence (AI); Dental disease; Diagnosis; Panoramic radiograph; Preliminary reading; | |
DOI : 10.1186/s12903-023-03027-6 | |
received in 2023-01-22, accepted in 2023-05-09, 发布年份 2023 | |
来源: Springer | |
【 摘 要 】
BackgroundArtificial 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.MethodsThe 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).ResultsSensitivity, 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).ConclusionsThe 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.【 授权许可】
CC BY
© The Author(s) 2023
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202309071135780ZK.pdf | 2254KB | download | |
Fig. 1 | 51KB | Image | download |
41116_2023_37_Article_IEq1.gif | 1KB | Image | download |
41116_2023_37_Article_IEq3.gif | 1KB | Image | download |
Fig. 1 | 120KB | Image | download |
MediaObjects/13011_2023_539_MOESM1_ESM.docx | 29KB | Other | download |
41116_2023_37_Article_IEq18.gif | 1KB | Image | download |
【 图 表 】
41116_2023_37_Article_IEq18.gif
Fig. 1
41116_2023_37_Article_IEq3.gif
41116_2023_37_Article_IEq1.gif
Fig. 1
【 参考文献 】
- [1]
- [2]
- [3]
- [4]
- [5]
- [6]
- [7]
- [8]
- [9]
- [10]
- [11]
- [12]
- [13]
- [14]
- [15]
- [16]
- [17]
- [18]
- [19]
- [20]
- [21]
- [22]
- [23]
- [24]
- [25]
- [26]
- [27]
- [28]
- [29]
- [30]
- [31]
- [32]
- [33]
- [34]
- [35]
- [36]
- [37]
- [38]
- [39]
- [40]
- [41]
- [42]
- [43]
- [44]