An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm
An efficient method for building a database of diatom populations for drowning site inference using a deep learning algorithm
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使用深度学习算法构建用于溺水地点推断的硅藻种群数据库的有效方法
DOI：
10.1007/s00414-020-02497-5
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发表时间：
2021-01-03
影响因子：
2.1
通讯作者：
Huang, Ping
中科院分区：
文献类型：
--
作者：
Zhang, Ji;Zhou, Yuanyuan;Huang, Ping
需要建立特定水体中硅藻种群的季节性或月度数据库，以推断溺水身体的溺水地点。然而，现有的硅藻检测方法费力、耗时、成本高，通常需要特定的专业知识。在这项研究中，我们开发了一个基于人工智能(AI)的系统，以取代人工形态检查，能够在物种水平上对硅藻进行识别和分类。在两天内，该系统收集了上海黄浦江和苏州河的硅藻剖面信息，中国说。在动物实验中，通过改进的Jensen-Shannon(JS)离散度来评估肺组织和水样中硅藻分布的相似性，得出了92.31%的预测准确率。考虑到它的高效和简单，我们建议的方法被认为比现有的方法更适用于对相互关联的河流段的硅藻种群进行季节性或月度水质监测，这将有助于警方缩小调查范围，以确认沉没身体的身份。
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.