Wang Ziwei, Zhang Fuyuan, Wang Linlin, Yuan Huiya, Guan Dawei, Zhao Rui
Department of Forensic Pathology, China Medical University School of Forensic Medicine, Shenyang, China.
Liaoning Province Key Laboratory of Forensic Bio-evidence Science, Shenyang, China.
Front Microbiol. 2022 Oct 4;13:1034051. doi: 10.3389/fmicb.2022.1034051. eCollection 2022.
Postmortem interval (PMI) estimation has always been a major challenge in forensic science. Conventional methods for predicting PMI are based on postmortem phenomena, metabolite or biochemical changes, and insect succession. Because postmortem microbial succession follows a certain temporal regularity, the microbiome has been shown to be a potentially effective tool for PMI estimation in the last decade. Recently, artificial intelligence (AI) technologies shed new lights on forensic medicine through analyzing big data, establishing prediction models, assisting in decision-making, etc. With the application of next-generation sequencing (NGS) and AI techniques, it is possible for forensic practitioners to improve the dataset of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, descriptions of their ecological function, and even their application in legal medicine. This review describes the postmortem succession of the microbiome in cadavers and their surroundings, and summarizes the application, advantages, problems, and future strategies of AI-based microbiome analysis for PMI estimation.
死后间隔时间（PMI）的估计一直是法医学中的一项重大挑战。传统的预测PMI的方法基于死后现象、代谢物或生化变化以及昆虫演替。由于死后微生物演替遵循一定的时间规律，在过去十年中，微生物组已被证明是一种潜在有效的PMI估计工具。最近，人工智能（AI）技术通过分析大数据、建立预测模型、协助决策等，为法医学带来了新的曙光。随着下一代测序（NGS）和AI技术的应用，法医从业者有可能改善微生物群落数据集，并获得有关特定生态系统清单、群落多样性量化、其生态功能描述以及甚至其在法医学中的应用的详细信息。本综述描述了尸体及其周围环境中微生物组的死后演替，并总结了基于AI的微生物组分析在PMI估计中的应用、优势、问题和未来策略。
