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dc.contributor.authorLi, Haoyang
dc.contributor.authorZhou, Juexiao
dc.contributor.authorZhou, Yi
dc.contributor.authorChen, Qiang
dc.contributor.authorShe, Yangyang
dc.contributor.authorGao, Feng
dc.contributor.authorXu, Ying
dc.contributor.authorChen, Jieyu
dc.contributor.authorGao, Xin
dc.date.accessioned2021-07-12T06:44:02Z
dc.date.available2021-07-12T06:44:02Z
dc.date.issued2021-06-22
dc.date.submitted2021-01-19
dc.identifier.citationLi, H., Zhou, J., Zhou, Y., Chen, Q., She, Y., Gao, F., … Gao, X. (2021). An Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs. Frontiers in Physiology, 12. doi:10.3389/fphys.2021.655556
dc.identifier.issn1664-042X
dc.identifier.issn1664-042X
dc.identifier.pmid34239448
dc.identifier.doi10.3389/fphys.2021.655556
dc.identifier.urihttp://hdl.handle.net/10754/670133
dc.description.abstractPeriodontitis 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.
dc.description.sponsorshipThe research reported in this study was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under award nos. BAS/1/1624-01, FCC/1/1976-23-01, FCC/1/1976-26-01, REI/1/0018-01-01, REI/1/4216-01-01, REI/1/4437-01-01, REI/1/4473-01-01, and URF/1/4098-01-01, the Fundamental Research Funds for the Central Universities (No. 20ykpy05 to FG), and the Sun Yat-sen University 100 Top Talent Scholars Program—China (No. P20190217202203617 to FG).
dc.publisherFrontiers Media SA
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fphys.2021.655556/full
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPanoramic radiograph
dc.subjectComputer-aided Diagnostics
dc.subjectInterpretable Model
dc.subjectPeriodontitis Diagnosis
dc.subjectTeeth Segmentation And Numbering
dc.titleAn Interpretable Computer-Aided Diagnosis Method for Periodontitis From Panoramic Radiographs.
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalFrontiers in physiology
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionCancer Systems Biology Center, The China-Japan Union Hospital, Jilin University, Changchun, China.
dc.contributor.institutionCollege of Computer Science and Technology, Jilin University, Changchun, China.
dc.contributor.institutionDepartment of Biology, Southern University of Science and Technology, Shenzhen, China.
dc.contributor.institutionDepartment of Biochemistry and Molecular Biology and Institute of Bioinformatics, University of Georgia, Athens, GA, United States.
dc.contributor.institutionThe Affiliated Stomatological Hospital of Soochow University, Soochow, China.
dc.contributor.institutionDepartment of Stomatology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
dc.contributor.institutionDepartment of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
dc.identifier.volume12
kaust.personLi, Haoyang
kaust.personZhou, Juexiao
kaust.personGao, Xin
kaust.grant.numberBAS/1/1624-01
kaust.grant.numberFCC/1/1976-23-01
kaust.grant.numberFCC/1/1976-26-01
kaust.grant.numberREI/1/0018-01-01
kaust.grant.numberREI/1/4216-01-01
kaust.grant.numberREI/1/4437-01-01
kaust.grant.numberREI/1/4473-01-01
kaust.grant.numberURF/1/4098-01-01
dc.date.accepted2021-05-31
refterms.dateFOA2021-07-12T06:45:40Z
kaust.acknowledged.supportUnitBAS
kaust.acknowledged.supportUnitOffice of Sponsored Research (OSR)


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This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Except where otherwise noted, this item's license is described as This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.