Tokushima University / Educator and Researcher Directory --- Shan, Xiao

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Tokushima University ⟩ University Hospital ⟩ Central Clinical Facilities ⟩ Medical IT Center ⟩
Tokushima University ⟩ Research Clusters ⟩ Research Clusters (Registered) ⟩ 2203014 AIを活用した食事摂取量自動判定による栄養管理システムの構築 ⟩
Tokushima University ⟩ Research Clusters ⟩ Research Clusters (Registered) ⟩ 2203019 AIを用いた糖尿病患者に対する食事管理システムの開発 ⟩

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Research

Field of Study

Subject of Study

Book / Paper

Academic Paper (Judged Full Paper):

1.	Yuxiang Zhou, XIN KANG, Fuji Ren, Huimin Lu, Satoshi Nakagawa and Xiao Shan : A Multi-attention and Depthwise Separable Convolution Network for Medical Image Segmentation, Neurocomputing, Vol.15, No.1, 121-129, 2024. (Keyword) U-Net / dual attention / attention gate / depthwise separable convolution / medical image segmentation (Tokushima University Institutional Repository) ● Metadata: 118693 (Link to Publication Site) ● Publication site (DOI): 10.1016/j.neucom.2023.126970 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.neucom.2023.126970 (Tokushima University Institutional Repository: 118693, DOI: 10.1016/j.neucom.2023.126970)
2.	Masato Tagi, Mari Tajiri, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki and Jun Hirose : Accuracy of an Artificial Intelligence-Based Model for Estimating Leftover Liquid Food in Hospitals: Validation Study., JMIR Formative Research, Vol.6, No.5, e35991, 2022. (Summary) An accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable. The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation. The accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation. The mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R value for the total was equal in terms of accuracy between the AI and visual estimations. The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement. (Tokushima University Institutional Repository) ● Metadata: 117555 (Link to Publication Site) ● Publication site (DOI): 10.2196/35991 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35536638 ● Search Scopus @ Elsevier (PMID): 35536638 ● Search Scopus @ Elsevier (DOI): 10.2196/35991 (Tokushima University Institutional Repository: 117555, DOI: 10.2196/35991, PubMed: 35536638)

Proceeding of International Conference:

1.	Yuxiang Zhou, XIN KANG, Fuji Ren, Satoshi Nakagawa and Xiao Shan : DEU-Net: Dual Encoder U-Net for 3D Medical Image Segmentation, The 22nd International Conference on Computer and Information Technology, 1-7, Nov. 2023. (Link to Publication Site) ● Publication site (DOI): 10.1109/TrustCom60117.2023.00382 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85195458711 (DOI: 10.1109/TrustCom60117.2023.00382, Elsevier: Scopus)

Proceeding of Domestic Conference:

1.	Masato Tagi, Xiao Shan, 岸上雅哉, 境貴司, 加藤裕司, Kazuo Okura and Jun Hirose : ランサムウェアによるサイバー攻撃に備えた病院情報システムのバックアップシステムの構築, 医療情報学連合大会論文集 43回, 973-974, Nov. 2023.
2.	Xiao Shan, Masato Tagi, 小西健史, Hiroyasu Mori, Munehide Matsuhisa and Jun Hirose : AIを用いた糖尿病患者に対する食事提案システムの開発, 医療情報学連合大会論文集 43回, 995-997, Nov. 2023.
3.	Masato Tagi, 田尻真梨, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, 久保田雅則, 天野宗佑 and Jun Hirose : Clinical validation of an AI-based system for estimating intake hospital liquid food, 第41回医療情報学連合大会, Nov. 2021.
4.	Masato Tagi, 田尻真梨, Yasuhiro Hamada, Yoshifumi Wakata, Xiao Shan, Kazumi Ozaki, 久保田雅則, 天野宗佑 and Jun Hirose : Validation of an AI-based model for estimating leftover liquid food in hospitals, 日本医療情報学会春季学術大会, Vol.25, 70-71, Jun. 2021.

Et cetera, Workshop:

1.	Masato Tagi, Jun Hirose, Yoshifumi Wakata and Xiao Shan : Visualization of the hospital network flow and the security alert function by automatic analysis of NetFlow data : Evaluation of the usefulness of security system, 月刊新医療, Vol.47, No.11, 38-41, Nov. 2020. (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1523669555798364544 (CiNii: 1523669555798364544)
2.	Jun Hirose, Yoshifumi Wakata, Masato Tagi and Xiao Shan : With Corona時代の医療情報担当部門の医学部卒前教育・業務支援のありかた∼徳島大学の取組み∼, Sep. 2020.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

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