1. Fully automated film mounting in dental radiography: a deep learning model
- Author
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Yu-Chun Lin, Meng-Chi Chen, Cheng-Hsueh Chen, Mu-Hsiung Chen, Kang-Yi Liu, and Cheng-Chun Chang
- Subjects
Radiography ,Dental ,Deep learning ,Medical technology ,R855-855.5 - Abstract
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
- Published
- 2023
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