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Abstract 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

Background/purpose: In Taiwan, the dental radiology has a history of more than 100 years. However, medical radiation schools or teaching hospitals have not yet established a set of standard learning content for dental radiology internship. Materials and methods: We used the observational method and dental radiology teaching survey to explore the connotation of clinical dental radiology education for medical radiation students in Taiwan. Results: There were 64 dental departments of hospitals with their own independent dental radiology departments in Taiwan in 2022. Among them, 58 (90.63%) were in the teaching hospitals. Of the 58 dental radiology departments in the teaching hospitals, 40 (68.97%) had medical radiation interns in their dental radiology departments. There was a complete set of seven training items for dental radiology internship. The mean training items in the medical centers (4.94) was significantly higher than that in the regional hospital (3.9) (P

This study examined whether interventions through barrier-free home environment improvements could reduce family caregivers' care stress, improve their family functions, and increase their quality of life. This study recruited family caregivers of older people with disabilities from a long-term care management center in central Taiwan. These older people required improvements related to a barrier-free home environment. A pretest was conducted before and a post-test was conducted 2 months after the improvements. The content of the pretest and the posttest questionnaires included: demographic characteristics, Caregiver stress scale, Family functions scale, World Health Organization Quality of Life-BREF (WHOQOL-BREF) Questionnaire. This study recruited 72 family caregivers; the average age was 56.25 ± 12.99 years. The results indicated that interventions through barrier-free home environment improvements could significantly reduce the family caregivers' care stress, improve their family functions (e.g., reducing conflicts), and enhance their quality of life. Additionally, after intervention, the family cohesion of family caregivers caring for those with mild disability improved to a greater extent than did that of those caring for people with other disease severities. This study revealed that interventions involving barrier-free home environment improvements have positive effects on family caregivers. [ABSTRACT FROM AUTHOR]