Wang Q, Lin H C, Xu J R, Huang P, Wang Z Y
Teaching and Research Section of Forensic Medicine, College of Basic Medicine, Chongqing Medical University, Chongqing 400016, China.
School of Forensic Medicine, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China.
Fa Yi Xue Za Zhi. 2018 Oct;34(5):459-467. doi: 10.12116/j.issn.1004-5619.2018.05.002. Epub 2018 Oct 25.
The researches on postmortem interval (PMI) estimation are very important and meaningful in forensic science. PMI estimation is also an important issue that must be solved in practice of forensic pathology. There are many defects existing in traditional methods for PMI estimation, so it is imperative to introduce new pathways. With the emergence of various new technologies, the researches on PMI estimation have a tendency from simple to complex with a growth of data. The present review firstly summarizes a series of methods used for PMI estimation, and then gives an outlook for the application of artificial intelligence algorithms in this field.
死后间隔时间(PMI)估计的研究在法医学中非常重要且具有意义。PMI估计也是法医病理学实践中必须解决的一个重要问题。传统的PMI估计方法存在许多缺陷,因此引入新途径势在必行。随着各种新技术的出现,PMI估计的研究随着数据的增长有从简单到复杂的趋势。本综述首先总结了一系列用于PMI估计的方法,然后对人工智能算法在该领域的应用进行了展望。