| 言語種別 | 英語 |
|---|---|
| 発行・発表の年月 | 2021/09/01 |
| 形態種別 | 【論文】研究論文（学術雑誌）＜査読あり＞ |
| 査読 | 査読あり |
| 標題 | Development of an age estimation method for bones based on machine learning using post-mortem computed tomography images of bones. |
| 執筆形態 | 共著 |
| 掲載誌名 | Forensic Imaging |
| 掲載区分 | 国外 |
| 出版社・発行元 | ScienceDirect |
| 巻・号・頁 | 26(200477) |
| 総ページ数 | 10 |
| 担当範囲 | In this research, I developed and verified all the software, from data input to three-dimensional homology modeling, machine learning, and verification using double cross-validation. |
| 著者・共著者 | ◎Kazuhiko Imaizumi, Shiori Usui, Kei Taniguchi, Yoshinori Ogawa, Takeshi Nagata,Kazunori Kaga, Hideyuki Hayakawa, Seiji Shiotani |
| 概要 | Materials and Methods: This study used PMCT images of the vertebral body, ischial tuberosity, iliac crest, and femur, which were transformed into homologous models. Wavelet transform was conducted to extract high-frequency components. Dimensionality reductions were conducted with principal component analysis and partial least squares regression (PLS).
A 10-fold double-looped cross-validation was conducted and estimation accuracies were verified with the mean absolute errors and correlation coefficients (r) between the actual and estimated ages. Results: and Conclusion: Preprocessing with 2D-DWT and PLS obtained good results. Of the ML methods examined, support vector regression with radial basis function kernel achieved the highest accuracy, with an optimum mean absolute error and r of 7.92 (male vertebral body) and 0.837 (female ischial tuberosity), respectively. |
| researchmap用URL | https://www.sciencedirect.com/science/article/pii/S2666225621000488 |