Abstract
Periodontitis is a prevalent and irreversible chronic inflammatory disease both in developed and developing countries, and affects about 20–50% of the global population. The tool for automatically diagnosing periodontitis is highly demanded to screen at-risk people for periodontitis and its early detection could prevent the onset of tooth loss, especially in local communities and health care settings with limited dental professionals. In the medical field, doctors need to understand and trust the decisions made by computational models and developing interpretable models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable method called Deetal-Perio to predict the severity degree of periodontitis in dental panoramic radiographs. In our method, alveolar bone loss (ABL), the clinical hallmark for periodontitis diagnosis, could be interpreted as the key feature. To calculate ABL, we also propose a method for teeth numbering and segmentation. First, Deetal-Perio segments and indexes the individual tooth via Mask R-CNN combined with a novel calibration method. Next, Deetal-Perio segments the contour of the alveolar bone and calculates a ratio for individual tooth to represent ABL. Finally, Deetal-Perio predicts the severity degree of periodontitis given the ratios of all the teeth. The Macro F1-score and accuracy of the periodontitis prediction task in our method reach 0.894 and 0.896, respectively, on Suzhou data set, and 0.820 and 0.824, respectively on Zhongshan data set. The entire architecture could not only outperform state-of-the-art methods and show robustness on two data sets in both periodontitis prediction, and teeth numbering and segmentation tasks, but also be interpretable for doctors to understand the reason why Deetal-Perio works so well.
Highlights
As for the teeth numbering and segmentation, Wirtz et al (2018) proposed a coupled shape model in conjunction with a neural network by using panoramic radiographs
Each radiograph has a high resolution of 1,480 ∗ 2,776 pixels and was annotated following the Fédération Dentaire Internationale (FDI) numbering system to get the contours of teeth and their labels as the ground truth (GT)
The binary and multi-class classification of Mask R-CNN were trained starting from pre-trained COCO weight and fine-tuned on our panoramic radiographs
Summary
As for the teeth numbering and segmentation, Wirtz et al (2018) proposed a coupled shape model in conjunction with a neural network by using panoramic radiographs. Chen et al (2019) used faster regions with CNN features to detect and number teeth in dental periapical films They proposed three post-processing techniques to improve the numbering performance. Cui et al (2019) used deep CNN to achieve automatic and accurate tooth instance segmentation and identification from cone-beam CT (CBCT) images. They extracted the edge map from the CBCT image to enhance image contrast along shape boundaries. The edge map and input images were passed through 3D Mask R-CNN with encoded teeth spatial relationships Their method produced accurate instance segmentation and identification results automatically
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