iPSCs由来自己拍動細胞塊における細胞拍動の加速度ベクトルに基づく3次元拍動点群特徴量を用いたロバストな機械学習型分化度判定法

• 資料種別: 記事
• タイトル: iPSCs由来自己拍動細胞塊における細胞拍動の加速度ベクトルに基づく3次元拍動点群特徴量を用いたロバストな機械学習型分化度判定法
• 著者・編者: ミシェンコ イェヴゲニイ; 田邉 造; 青山 純也; 宮城 泰雄
• 著者標目: ミシェンコ イェヴゲニイ; 田邉 造; 青山 純也; 宮城 泰雄
• 並列タイトル等: Robust Machine Learning for Differentiation using 3D Point Cloud Based Features Constructed with Acceleration Vector of Embryoid Body Made from iPs Cells
• タイトル（掲載誌）: 生体医工学 = Transactions of Japanese Society for Medical and Biological Engineering : 日本エム・イー学会誌
• 巻号年月日等（掲載誌）: 63(2・3)=323:2025.6
• 掲載巻: 63
• 掲載号: 2・3
• 掲載通号: 323
• 掲載ページ: 98-103
• 掲載年月日（W3CDTF）: 2025-06
• ISSN（掲載誌）: 1347-443X
• ISSN-L（掲載誌）: 1347-443X
• 出版事項（掲載誌）: 東京 : 日本エム・イー学会 ; 2002-
• 出版地（国名コード）: JP
• 本文の言語コード: jpn
• 件名標目: embryoid body made from iPS cells; dynamic analysis; acceleration; point cloud pulsation; cellular differentiation method
• NDLC: ZS18
• 対象利用者: 一般
• 所蔵機関: 国立国会図書館
• 請求記号: Z19-108
• 連携機関・データベース: 国立国会図書館 : 国立国会図書館雑誌記事索引; https://ndlsearch.ndl.go.jp
• 書誌ID（NDLBibID）: 034264992; http://id.ndl.go.jp/bib/034264992
• 整理区分コード: 632

• 要約等: <p>Research on induced pluripotent stem (iPS) cells is broadly categorized into two main areas: (i) replicating disease mechanisms in vitro to elucidate pathophysiology and explore new drug therapies, and (ii) efficient and safe production of iPS cells for regenerative medicine, to restore lost functions within the body. Although iPS cells have the potential to differentiate into nearly any cell type in the body, establishing standardized methods for the quantitative evaluation of differentiation remains challenging. Therefore, a method that objectively evaluates the differentiation of iPS cells is required. This paper proposes a machine learning method to assess differentiation based on the dynamic analysis of embryoid body made from iPS cells, which relies on features extracted from a three-dimensional pulsation point group derived from the acceleration and angles of embryoid body made from iPS cells pulsations over time. The process consists of the following steps: (Step 1) Utilizing Gunnar Farneback optical flow analysis to determine acceleration vectors and their directions in a hue, saturation, and value (HSV) color space from the subtle dynamics of embryoid body made from iPS cells. (Step 2) Dividing the directions of the acceleration vectors into 36 segments of 10°and summing them to form a total sum acceleration vector that represents a three-dimensional pulsation point group with respect to time and angles. (Step 3) Constructing a differentiation assessment method of embryoid body made from iPS cells using machine learning based on the objective features of periodicity, convergence, and variability extracted from the three-dimensional pulsation point group. The effectiveness of the proposed features for differentiation assessment method of embryoid body made from iPS cells was validated through computer simulations, which ascertained that the method of embryoid body made from iPS cells achieved an accuracy of 84%and F-score of 80%. This highlights the robustness and high precision of the proposed machine learning-based differentiation assessment method of embryoid body made from iPS cells, which focuses on the quantitative features of the acceleration vectors within three-dimensional pulsation point groups.</p>
• DOI: 10.11239/jsmbe.63.98
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 科学技術振興機構 : J-STAGE; http://www.jstage.jst.go.jp

• 要約等: <p>Research on induced pluripotent stem (iPS) cells is broadly categorized into two main areas: (i) replicating disease mechanisms in vitro to elucidate pathophysiology and explore new drug therapies, and (ii) efficient and safe production of iPS cells for regenerative medicine, to restore lost functions within the body. Although iPS cells have the potential to differentiate into nearly any cell type in the body, establishing standardized methods for the quantitative evaluation of differentiation remains challenging. Therefore, a method that objectively evaluates the differentiation of iPS cells is required. This paper proposes a machine learning method to assess differentiation based on the dynamic analysis of embryoid body made from iPS cells, which relies on features extracted from a three-dimensional pulsation point group derived from the acceleration and angles of embryoid body made from iPS cells pulsations over time. The process consists of the following steps: (Step 1) Utilizing Gunnar Farneback optical flow analysis to determine acceleration vectors and their directions in a hue, saturation, and value (HSV) color space from the subtle dynamics of embryoid body made from iPS cells. (Step 2) Dividing the directions of the acceleration vectors into 36 segments of 10°and summing them to form a total sum acceleration vector that represents a three-dimensional pulsation point group with respect to time and angles. (Step 3) Constructing a differentiation assessment method of embryoid body made from iPS cells using machine learning based on the objective features of periodicity, convergence, and variability extracted from the three-dimensional pulsation point group. The effectiveness of the proposed features for differentiation assessment method of embryoid body made from iPS cells was validated through computer simulations, which ascertained that the method of embryoid body made from iPS cells achieved an accuracy of 84%and F-score of 80%. This highlights the robustness and high precision of the proposed machine learning-based differentiation assessment method of embryoid body made from iPS cells, which focuses on the quantitative features of the acceleration vectors within three-dimensional pulsation point groups.</p>
• DOI: 10.11239/jsmbe.63.98; https://doi.org/10.11239/jsmbe.63.98
• 関連情報（URI）: http://id.ndl.go.jp/bib/034264992; https://ndlsearch.ndl.go.jp/books/R000000004-I034264992; https://search.jamas.or.jp/link/ui/2025292365
• 連携機関・データベース: 国立情報学研究所 : CiNii Research; https://cir.nii.ac.jp/
• 提供元機関・データベース: Japan Link Center; https://japanlinkcenter.org/top; 雑誌記事索引データベース; https://ndlsearch.ndl.go.jp
• 書誌ID（NDLBibID）: 034264992; http://id.ndl.go.jp/bib/034264992