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
PMID: 33392655

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.

Authors

  • Ji Zhang
    Department of Neurology, Xiangya Hospital, Central South University, Jiangxi, Nanchang, 330006, Jiangxi, China.
  • Yuanyuan Zhou
    Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China; Department of Forensic Medicine, Inner Mongolia Medical University, Huhhot, Inner Mongolia, 010110, China.
  • Duarte Nuno Vieira
    Department of Forensic Medicine, Ethics and Medical Law, Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
  • Yongjie Cao
    Department of Forensic Medicine, Nanjing Medical University, Nanjing, Jiangsu, People's Republic of China.
  • Kaifei Deng
    Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China.
  • Qi Cheng
    Institute of Intelligent System and Bioinformatics, College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
  • Yongzheng Zhu
    School of Forensic Medicine, Shanxi Medical University, Taiyuan, Shanxi, People's Republic of China.
  • Jianhua Zhang
  • Zhiqiang Qin
    Department of Urology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
  • Kaijun Ma
    Shanghai Key Laboratory of Crime Scene Evidence, Institute of Criminal Science and Technology, Shanghai Municipal Public Security Bureau, Shanghai, People's Republic of China. makaijun@sina.cn.
  • Yijiu Chen
    Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, 200063, China. Electronic address: cyj1347@163.com.
  • Ping Huang
    Division of HIV/AIDS Prevention, Centers for Disease Control and Prevention, Atlanta, GA, USA.

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