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An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm

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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.

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Funding

This work is supported by the Natural Science Foundation of China (Nos. 81801873, 81722027, and 81671869), grants from the Ministry of Finance (No. GY2020G-2), the Science and Technology Committee of Shanghai Municipality (Nos. 17DZ2273200 and 19DZ2292700), and Shanghai Key Laboratory of Forensic Medicine [KF1802].

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Author notes

  1. Ji Zhang and Yuanyuan Zhou contributed equally to this work.

Authors and Affiliations

  1. Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, People’s Republic of China

    Ji Zhang, Yuanyuan Zhou, Kaifei Deng, Jianhua Zhang, Zhiqiang Qin, Yijiu Chen & Ping Huang

  2. Department of Forensic Medicine, Inner Mongolia Medical University, Huhhot, Inner Mongolia, People’s Republic of China

    Yuanyuan Zhou

  3. Department of Forensic Medicine, Ethics and Medical Law, Faculty of Medicine, University of Coimbra, Coimbra, Portugal

    Duarte Nuno Vieira

  4. Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of China

    Yongjie Cao

  5. Department of Forensic Medicine, Guizhou Medical University, Guiyang, Guizhou, People’s Republic of China

    Qi Cheng

  6. School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi, People’s Republic of China

    Yongzheng Zhu

  7. Shanghai Key Laboratory of Crime Scene Evidence, Institute of Criminal Science and Technology, Shanghai Municipal Public Security Bureau, Shanghai, People’s Republic of China

    Kaijun Ma

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  1. Ji Zhang
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Correspondence to Kaijun Ma, Yijiu Chen or Ping Huang.

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Zhang, J., Zhou, Y., Vieira, D.N. et al. An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm. Int J Legal Med 135, 817–827 (2021). https://doi.org/10.1007/s00414-020-02497-5

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  • DOI: https://doi.org/10.1007/s00414-020-02497-5

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