Publication: Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks
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Issued Date
2020
Resource Type
File Type
application/pdf
ISSN
22337822
Other identifier(s)
2-s2.0-85087509466
Rights Holder(s)
Scopus
Bibliographic Citation
Imaging Science in Dentistry. Vol 50, No.2 (2020), p.169-174
Suggested Citation
Thanathornwong B., Suebnukarn S. Automatic detection of periodontal compromised teeth in digital panoramic radiographs using faster regional convolutional neural networks. Imaging Science in Dentistry. Vol 50, No.2 (2020), p.169-174. doi:10.5624/isd.2020.50.2.169 Retrieved from: https://hdl.handle.net/20.500.14740/4531
Author(s)
Abstract
Purpose: Periodontal disease causes tooth loss and is associated with cardiovascular diseases, diabetes, and rheumatoid arthritis. The present study proposes using a deep learning-based object detection method to identify periodontally compromised teeth on digital panoramic radiographs. A faster regional convolutional neural network (faster R-CNN) which is a state-of-the-art deep detection network, was adapted from the natural image domain using a small annotated clinical data-set. Materials and Methods: In total, 100 digital panoramic radiographs of periodontally compromised patients were retrospectively collected from our hospital's information system and augmented. The periodontally compromised teeth found in each image were annotated by experts in periodontology to obtain the ground truth. The Keras library, which is written in Python, was used to train and test the model on a single NVidia 1080Ti GPU. The faster R-CNN model used a pretrained ResNet architecture. Results: The average precision rate of 0.81 demonstrated that there was a significant region of overlap between the predicted regions and the ground truth. The average recall rate of 0.80 showed that the periodontally compromised teeth regions generated by the detection method excluded healthiest teeth areas. In addition, the model achieved a sensitivity of 0.84, a specificity of 0.88 and an F-measure of 0.81. Conclusion: The faster R-CNN trained on a limited amount of labeled imaging data performed satisfactorily in detecting periodontally compromised teeth. The application of a faster R-CNN to assist in the detection of periodontally compromised teeth may reduce diagnostic effort by saving assessment time and allowing automated screening documentation. © 2020 Korean Academy of Oral and Maxillofacial Radiology.