@article{ref1,
title="An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm",
journal="International journal of legal medicine",
year="2021",
author="Qin, Zhiqiang and Zhang, Jianhua and Zhu, Yongzheng and Cheng, Qi and Deng, Kaifei and Cao, Yongjie and Vieira, Duarte Nuno and Zhou, Yuanyuan and Zhang, Ji and Huang, Ping and Chen, Yijiu and Ma, Kaijun",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Seasonal or monthly databases of the diatom populations in specific bodies of water are needed to infer the drowning site of a drowned body. However, existing diatom  testing methods are laborious, time-consuming, and costly and usually require  specific expertise. In this study, we developed an artificial intelligence  (AI)-based system as a substitute for manual morphological examination capable of  identifying and classifying diatoms at the species level. Within two days, the  system collected information on diatom profiles in the Huangpu and Suzhou Rivers of  Shanghai, China. In an animal experiment, the similarities of diatom profiles  between lung tissues and water samples were evaluated through a modified  Jensen-Shannon (JS) divergence measure for drowning site inference, reaching a  prediction accuracy of 92.31%. Considering its high efficiency and simplicity, our  proposed method is believed to be more applicable than existing methods for seasonal  or monthly water monitoring of diatom populations from sections of interconnected  rivers, which would help police narrow the investigation scope to confirm the  identity of an immersed body.<p /> <p>Language: en</p>",
language="en",
issn="0937-9827",
doi="10.1007/s00414-020-02497-5",
url="http://dx.doi.org/10.1007/s00414-020-02497-5"
}