Fully automated film mounting in dental radiography: a deep learning model.

BACKGROUND: Dental film mounting is an essential but time-consuming task in dental radiography, with manual methods often prone to errors. This study aims to develop a deep learning (DL) model for accurate automated classification and mounting of both intraoral and extraoral dental radiography. METHOD: The present study employed a total of 22,334 intraoral images and 1,035 extraoral images to train the model. The performance of the model was tested on an independent internal dataset and two external datasets from different institutes. Images were categorized into 32 tooth areas. The VGG-16, ResNet-18, and ResNet-101 architectures were used for pretraining, with the ResNet-101 ultimately being chosen as the final trained model. The model's performance was evaluated using metrics of accuracy, precision, recall, and F1 score. Additionally, we evaluated the influence of misalignment on the model's accuracy and time efficiency. RESULTS: The ResNet-101 model outperformed VGG-16 and ResNet-18 models, achieving the highest accuracy of 0.976, precision of 0.969, recall of 0.984, and F1-score of 0.977 (p < 0.05). For intraoral images, the overall accuracy remained consistent across both internal and external datasets, ranging from 0.963 to 0.972, without significant differences (p = 0.348). For extraoral images, the accuracy consistently achieved the highest value of 1 across all institutes. The model's accuracy decreased as the tilt angle of the X-ray film increased. The model achieved the highest accuracy of 0.981 with correctly aligned films, while the lowest accuracy of 0.937 was observed for films exhibiting severe misalignment of ± 15° (p < 0.001). The average time required for the tasks of image rotation and classification for each image was 0.17 s, which was significantly faster than that of the manual process, which required 1.2 s (p < 0.001). CONCLUSION: This study demonstrated the potential of DL-based models in automating dental film mounting with high accuracy and efficiency. The proper alignment of X-ray films is crucial for accurate classification by the model.

Overview of dental radiation technologists in Taiwan.

Background/purpose: In Taiwan, there is no independent licensing system for dental radiation technologists. A licensed medical radiation technologist who engages in dental radiology is the so-called dental radiation technologist. This study explored mainly the profile of dental radiation technologists in Taiwan. Materials and methods: This study used the methods of documentary analysis, dental radiation manpower survey, and the secondary data analysis to find the profile of dental radiation technologists in Taiwan. Results: There were currently 59 dental radiology departments and 101 dental radiation technologists (29 males and 72 females) in 57 hospitals and their branches. Of the 101 dental radiation technologists, 56 worked in the medical centers, 28 in the regional hospitals, and 17 in the district hospitals. More than half of the dental radiation technologists were concentrated in the medical centers (55.45%, 56/101) or the northern region of Taiwan (57.43%, 58/101), especially in the northern medical centers (30.69%, 31/101). Conclusion: In Taiwan, the manpower of dental radiation technologists is insufficient, and dental radiation technologists usually work in the dental departments of the hospitals. A large number of clinic dentists lack dental radiation technologists to assist in dental radiology works and the clinic dentists have to perform the dental radiology works by themselves. Therefore, a dental radiology education system should be established to design innovative dental radiology courses for radiological technology students. This in turn can provide a new practice direction for medical radiation technologists and expand their potential participation in the field of dental radiology.