Research advances in forensic diatom testing

Yuanyuan Zhou; Yongjie Cao; Jiao Huang; Kaifei Deng; Kaijun Ma; Tianye Zhang; Liqin Chen; Ji Zhang; Ping Huang

doi:10.1080/20961790.2020.1718901

Research advances in forensic diatom testing

Forensic Sci Res. 2020 Mar 23;5(2):98-105. doi: 10.1080/20961790.2020.1718901. eCollection 2020.

Authors

Yuanyuan Zhou^{1

2}, Yongjie Cao³, Jiao Huang⁴, Kaifei Deng¹, Kaijun Ma⁵, Tianye Zhang⁵, Liqin Chen², Ji Zhang¹, Ping Huang¹

Affiliations

¹ Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai, China.
² Department of Forensic Medicine, Inner Mongolia Medical University, Huhhot, China.
³ Department of Forensic Medicine, Nanjing Medical University, Nanjing, China.
⁴ Department of Forensic Medicine, Xuzhou Medical University, Xuzhou, China.
⁵ Institute of Forensic Science Shanghai Municipal Public Security Bureau, Shanghai, China.

Abstract

In forensic practice, it is difficult to determine whether a dead body in the water resulted from drowning or from disposal after death. Diatom testing is currently an important supporting technique for the determination of death by drowning and of drowning sites, even though it is a time-consuming and laborious task. This article reviews the development of diatom testing over the decades and discusses a new method for the potential application of deep learning in diatom testing.

Keywords: Forensic sciences; deep learning; diatom; drowning; forensic pathology.

Publication types

Review

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China [grand numbers 81801873, 81722027 and 81671869], the Science and Technology Committee of Shanghai Municipality [grand numbers 17DZ2273200 and 19DZ2292700], and Key Laboratory of Forensic Medicine [grand numbers KF1802].