선택한 단어 수는 입니다.
최소 단어 이상 선택하여야 합니다.
최소 단어 이상 선택하여야 합니다.
선택한 단어 수는 30입니다.
최대 10 단어까지만 선택 가능합니다.
최대 10 단어까지만 선택 가능합니다.
가설 설정
- The original panoramic radiograph (A) and the bounding box output from the faster R-CNN (B) when using 5-fold cross validation showing correct prediction (C) of all periodontally compromised teeth and a false-positive prediction of 1 healthy tooth (number 45). P: periodontally compromised tooth, H: healthy tooth.
제안 방법
- The experts were asked to draw a rectangular bounding box to frame each intact tooth (including the crown and root). Due to differences in the manual segmentation per-formed by the 3 experts, this study used the largest intersection area of the annotations made by all 3 experts during the training and testing phase.
- Gradient descent computation and updates were carried out by a stochastic gradient descent optimizer. To assess the performance of the faster R-CNN object detection method in detecting periodontally compromised teeth on panoramic radiographs, this study employed the average recall rate (ARR) and average precision rate (APR)13 to evaluate the bounding box accuracy. Moreover, the sensitivity, specific-ity, and F-measure were calculated, as metrics that reflect the number of missed regions in periodontally compromised teeth and any false predictions for healthy teeth.
- 2. The original panoramic radiograph (A) and the bounding box output from the faster R-CNN (B) when using 5-fold cross validation showing correct prediction (C) of all periodontally compromised teeth and a false-positive prediction of 1 healthy tooth (number 45). P: periodontally compromised tooth, H: healthy tooth.
- 3. The original panoramic radiograph (A) and the bounding box output from the faster R-CNN (B) when using 5-fold cross validation showing correct prediction (C) of all healthy teeth and a false-negative prediction of 2 periodontally compromised teeth (numbers 17 and 31). P: periodontally compromised tooth, H: healthy tooth.
- 20 proposed 3 postprocessing techniques to supplement the baseline faster R-CNN according to certain prior domain knowledge. First, a filtering algorithm was constructed to delete over-lapping boxes (specifically, those associated with the same tooth) detected by the faster R-CNN. Next, a neural network model was implemented to detect missing teeth.
- We plan to conduct further clinical trials comparing the identification of periodontally compromised teeth using conventional clinical and radiographic findings with and without the support of the faster R-CNN. Other topics for future research include additional techniques for further improving the system output, such as applying more advanced augmentation techniques, extending the dataset, and using more recent CNN architectures.
대상 데이터
- All patients with periodontal disease who were examined and treated between January 2012 and December 2017 were identified. The dataset consisted of 100 anonymized digital panoramic radiographs collected using an Orthophos XG (Sirona, Bensheim, Germany). The mean age of the patients, who consisted of 42 men and 58 women, at the time of the oral examinations was 51.
이론/모형
- 10 Deep learning, specifi-cally as implemented by CNNs, has proven to be effective in various fields, such as detection, classification, segmentation, and several advanced object detection methods use deep learning. In this article, the faster R-CNN proposed by Ren et al. was used,8 which is a combination of the region proposal network (RPN) and the fast R-CNN model.
- This retrospective study used our institutional general dentistry registry database with approval from the institutional ethical committee (DENTSWU-EC16/2561). All patients with periodontal disease who were examined and treated between January 2012 and December 2017 were identified.
성능/효과
- 11 Teeth with a clinical attachment level of less than 3 mm were diagnosed as healthy. Teeth with bleeding on probing and a clinical attachment level below 6 mm or bone loss of less than 4 mm were classified as moderately periodontally compromised, and teeth with a clinical attachment level of greater than 6 mm and bone loss of more than 4 mm were diagnosed as severely periodontally compromised. 11 Moderately and severely periodontally compromised teeth were grouped together to form the periodontally compromised teeth group.
- 11 Moderately and severely periodontally compromised teeth were grouped together to form the periodontally compromised teeth group. In our dataset, 56.3% of teeth were healthy, and 43.7% were periodontally compromised.
- In summary, this study found that a faster R-CNN trained on a limited number of labeled imaging data had satisfactory detection ability for 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 enabling automated screening documentation.
후속연구
- In this study, the performance of the faster R-CNN could not be directly compared to the performance of experts since periodontally compromised teeth are not identified using only radiographic images. We plan to conduct further clinical trials comparing the identification of periodontally compromised teeth using conventional clinical and radiographic findings with and without the support of the faster R-CNN. Other topics for future research include additional techniques for further improving the system output, such as applying more advanced augmentation techniques, extending the dataset, and using more recent CNN architectures.
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