Research advances in forensic diatom testing
Bibliographic Data
| ID | 16964850 |
|---|---|
| Authors | Yuanyuan Zhou (0000-0001-8378-6296, Institute of Forensic Science), Yong-Jie Cao (Nanjing Medical University), Yongjie Cao, Jiao Huang (0000-0002-1234-499X, Xuzhou Medical College), Kaifei Deng (Institute of Forensic Science), Kaijun Ma (Institute of Forensic Science), Tianye Zhang (0000-0002-0699-3117, Institute of Forensic Science), Liqin Chen (0000-0002-6513-8217, Inner Mongolia Medical University, corresponding author), Ji Zhang (0000-0001-9544-9801, Institute of Forensic Science, corresponding author), Ping Huang (0000-0002-7573-715X, Institute of Forensic Science, corresponding author) |
| Year | 2020 |
| Volume | 5 |
| Issue | 2 |
| Pages | 98-105 |
| Publication date | 2020-04-02 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Forensic Sciences Research (JOURNAL) |
| Journal identifiers | ISSN: 2471-1411 • E-ISSN: 2096-1790 |
| Publisher | Shanghai Institute of Optics and Fine Mechanics (PUBLISHER • CN) |
| DOI | 10.1080/20961790.2020.1718901 |
| PMID | 32939425 |
| OpenAlex | W3012871098 |
| Language | EN |
| References cited | 32 |
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
Archaeology · Diatom · Forensic science · Computer Science · Forensic Anthropology and Bioarchaeology Studies · Forensic Entomology and Diptera Studies · History · Injury Epidemiology and Prevention · Geology · Oceanography
| Citation velocity | historical |
|---|---|
| Highly cited | No |