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Authors: Yusuke Moriyama 1 ; Chonho Lee 2 ; Susumu Date 2 ; Yoichiro Kashiwagi 3 ; Yuki Narukawa 3 ; Kazunori Nozaki 4 and Shinya Murakami 3

Affiliations: 1 Graduate School of Information Science and Technology, Osaka University, Osaka, Japan ; 2 Cybermedia Center, Osaka University, Osaka, Japan ; 3 Graduate School of Dentistry, Osaka University, Osaka, Japan ; 4 Osaka University Dental Hospital, Osaka, Japan

ISBN: 978-989-758-353-7

Keyword(s): Periodontal Disease, Periodontal Pocket, Convolutional Neural Networks, Deep Learning, Object Detection.

Abstract: This paper explores the feasibility of diagnostic imaging using a deep learning-based model, applicable to periodontal disease, especially periodontal pocket screening. Having investigated conventional approaches, we find two difficulties to estimate the pocket depth of teeth from oral images. One is the feature extraction of Region of Interest (ROI), which is pocket region, caused by the small ROI, and another is tooth identification caused by the high heterogeneity of teeth (e.g., in size, shape, and color). We propose a MapReduce-like periodontal pocket depth estimation model that overcomes the difficulties. Specifically, a set of MapTasks is executed in parallel, each of which only focuses on one of the multiple views (e.g., front, left, right, etc.) of oral images and runs an object detection model to extract the high-resolution pocket region images. After a classifier estimates pocket depth from the extracted images, ReduceTasks aggregate the pocket depth with respect to each po cket. Experimental results show that the proposed model effectively works to achieve the estimation accuracy to 76.5 percent. Besides, we verify the practical feasibility of the proposed model with 91.7 percent accuracy under the condition that a screening test judges severe periodontitis (6 mm or more). (More)

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Paper citation in several formats:
Moriyama, Y.; Lee, C.; Date, S.; Kashiwagi, Y.; Narukawa, Y.; Nozaki, K. and Murakami, S. (2019). A MapReduce-like Deep Learning Model for the Depth Estimation of Periodontal Pockets.In Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF, ISBN 978-989-758-353-7, pages 388-395. DOI: 10.5220/0007405703880395

@conference{healthinf19,
author={Yusuke Moriyama. and Chonho Lee. and Susumu Date. and Yoichiro Kashiwagi. and Yuki Narukawa. and Kazunori Nozaki. and Shinya Murakami.},
title={A MapReduce-like Deep Learning Model for the Depth Estimation of Periodontal Pockets},
booktitle={Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,},
year={2019},
pages={388-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007405703880395},
isbn={978-989-758-353-7},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: HEALTHINF,
TI - A MapReduce-like Deep Learning Model for the Depth Estimation of Periodontal Pockets
SN - 978-989-758-353-7
AU - Moriyama, Y.
AU - Lee, C.
AU - Date, S.
AU - Kashiwagi, Y.
AU - Narukawa, Y.
AU - Nozaki, K.
AU - Murakami, S.
PY - 2019
SP - 388
EP - 395
DO - 10.5220/0007405703880395

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