徳島大学 / 教育研究者総覧 --- 単暁

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徳島大学 ⟩ 病院 ⟩ 中央診療施設等 ⟩ 病院情報センター ⟩
徳島大学 ⟩ 研究クラスター群 ⟩ 研究クラスター群 (登録) ⟩ 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 ⟩
徳島大学 ⟩ 研究クラスター群 ⟩ 研究クラスター群 (登録) ⟩ 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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著書・論文

学術論文(審査論文):

1.	Masato Tagi, Yasuhiro Hamada, Xiao Shan, Kazumi Ozaki, Masanori Kubota, Sosuke Amano, Hiroshi Sakaue, Yoshiko Suzuki, Takeshi Konishi and Jun Hirose : A Food Intake Estimation System Using an Artificial Intelligence-Based Model for Estimating Leftover Hospital Liquid Food in Clinical Environments: Development and Validation Study., JMIR Formative Research, 8, e55218, 2024. (キーワード) Humans / 人工知能 (artificial intelligence) / Eating / Food Service, Hospital / Energy Intake (徳島大学機関リポジトリ) ● Metadata: 2012394 (出版サイトへのリンク) ● Publication site (DOI): 10.2196/55218 (文献検索サイトへのリンク) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 39500491 ● Search Scopus @ Elsevier (PMID): 39500491 ● Search Scopus @ Elsevier (DOI): 10.2196/55218 (徳島大学機関リポジトリ: 2012394, DOI: 10.2196/55218, PubMed: 39500491)
2.	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, 15, 1, 121-129, 2024. (キーワード) U-Net / dual attention / attention gate / depthwise separable convolution / medical image segmentation (徳島大学機関リポジトリ) ● Metadata: 2011407 (出版サイトへのリンク) ● Publication site (DOI): 10.1016/j.neucom.2023.126970 (文献検索サイトへのリンク) ● Search Scopus @ Elsevier (DOI): 10.1016/j.neucom.2023.126970 (徳島大学機関リポジトリ: 2011407, DOI: 10.1016/j.neucom.2023.126970)
3.	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, 6, 5, e35991, 2022. (要約) 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. (徳島大学機関リポジトリ) ● Metadata: 2010359 (出版サイトへのリンク) ● Publication site (DOI): 10.2196/35991 (文献検索サイトへのリンク) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 35536638 ● Search Scopus @ Elsevier (PMID): 35536638 ● Search Scopus @ Elsevier (DOI): 10.2196/35991 (徳島大学機関リポジトリ: 2010359, DOI: 10.2196/35991, PubMed: 35536638)

国際会議:

1.	Zhou Yuxiang, XIN KANG, Nakagawa Satoshi and Xiao Shan : A Dual Encoder U-Net for Multi-Scale 3D Medical Image Segmentation, 2024 International Symposium on Artificial Intelligence and Robotics, 1-9, Guilin, China, Sep. 2024.
2.	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. (出版サイトへのリンク) ● Publication site (DOI): 10.1109/TrustCom60117.2023.00382 (文献検索サイトへのリンク) ● Summary page in Scopus @ Elsevier: 2-s2.0-85195458711 (DOI: 10.1109/TrustCom60117.2023.00382, Elsevier: Scopus)

国内講演発表:

1.	田木真和, 単暁, 岸上雅哉, 境貴司, 加藤裕司, 大倉一夫, 廣瀬隼 : 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, 2023年11月.
2.	単暁, 田木真和, 小西健史, 森博康, 松久宗英, 廣瀬隼 : 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, 2023年11月.
3.	田木真和, 田尻真梨, 濵田康弘, 若田好史, 単暁, 尾崎和美, 久保田雅則, 天野宗佑, 廣瀬隼 : AIを用いた病院流動食の摂取量推定システムの臨床現場における精度検証, 第41回医療情報学連合大会, 2021年11月.
4.	田木真和, 田尻真梨, 濵田康弘, 若田好史, 単暁, 尾崎和美, 久保田雅則, 天野宗佑, 廣瀬隼 : AIを用いた病院流動食の残量推定の検証, 日本医療情報学会春季学術大会, 25, 70-71, 2021年6月.

その他・研究会:

1.	田木真和, 廣瀬隼, 若田好史, 単暁 : 院内通信の可視化と自動解析アラート機能によるセキュリティ強化策の評価 (総特集現場視点からセキュリティ対策を検証する) -- (強固かつ柔軟な安全対策の要諦), 月刊新医療, 47, 11, 38-41, 2020年11月. (文献検索サイトへのリンク) ● CiNii @ 国立情報学研究所 (CRID): 1523669555798364544 (CiNii: 1523669555798364544)
2.	廣瀬隼, 若田好史, 田木真和, 単暁 : 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∼, 2020年9月.

科学研究費補助金 (KAKEN Grants Database @ NII.ac.jp)

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