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    Automatic and Interpretable Model for Periodontitis Diagnosis in Panoramic Radiographs

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    Name:
    Li2020_Chapter_AutomaticAndInterpretableModel.pdf
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    6.898Mb
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    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Li, Haoyang
    Zhou, Juexiao
    Zhou, Yi
    Chen, Jieyu
    Gao, Feng
    Xu, Ying
    Gao, Xin cc
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Structural and Functional Bioinformatics Group
    Date
    2020-09-29
    Online Publication Date
    2020-09-29
    Print Publication Date
    2020
    Embargo End Date
    2021-10-02
    Permanent link to this record
    http://hdl.handle.net/10754/665823
    
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    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 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_44
    Publisher
    Springer International Publishing
    Conference/Event name
    23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
    ISBN
    9783030597122
    DOI
    10.1007/978-3-030-59713-9_44
    Additional Links
    http://link.springer.com/10.1007/978-3-030-59713-9_44
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-030-59713-9_44
    Scopus Count
    Collections
    Conference Papers; Structural and Functional Bioinformatics Group; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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