Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs
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Li2020_Chapter_AutomaticAndInterpretableModel.pdf
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Accepted manuscript
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Conference PaperKAUST Department
Computational Bioscience Research Center (CBRC)Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Structural and Functional Bioinformatics Group
Date
2020-09-29Online Publication Date
2020-09-29Print Publication Date
2020Embargo End Date
2021-10-02Permanent link to this record
http://hdl.handle.net/10754/665823
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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 community 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 machine learning models is crucial for disease diagnosis. Based on these considerations, we propose an interpretable machine learning 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 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.Citation
Li, H., Zhou, J., Zhou, Y., Chen, J., Gao, F., Xu, Y., & Gao, X. (2020). Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs. Lecture Notes in Computer Science, 454–463. doi:10.1007/978-3-030-59713-9_44Publisher
Springer International PublishingConference/Event name
23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020ISBN
9783030597122Additional Links
http://link.springer.com/10.1007/978-3-030-59713-9_44ae974a485f413a2113503eed53cd6c53
10.1007/978-3-030-59713-9_44