Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.

BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. METHODS: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. RESULTS: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. CONCLUSIONS: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

ESTIMATING RADIATION DOSE TO MAJOR ORGANS IN DENTAL X-RAY EXAMINATIONS: A PHANTOM STUDY.

The radiation doses absorbed by major organs of males and females were studied from three types of dental X-ray devices. The absorbed doses from cone-beam computed tomography (CBCT), panoramic and intraoral X-ray machines were in the range of 0.23-1314.85 µGy, and were observed to be high in organs and tissues located in or adjacent to the irradiated area, there were discrepancies in organ doses between male and female. Thyroid, salivary gland, eye lens and brain were the organs that received higher absorbed doses. The organ absorbed doses were considerably lower than the diagnostic reference level for dental radiography in China. The calculated effective radiation doses for males and females were 56.63, 8.15, 2.56 µSv and 55.18, 8.99, 2.39 µSv, respectively, when using CBCT, the panoramic X-ray machine and intraoral X-ray machine. The effective radiation dose caused by CBCT was much higher than those of panoramic and intraoral X-ray machines.