The postmortem interval (PMI) estimation is a key and difficult point in the practice of forensic medicine, and forensic scientists at home and abroad have been searching for objective, quantifiable and accurate methods of PMI estimation. With the development and combination of high-throughput sequencing technology and artificial intelligence technology, the establishment of PMI model based on the succession of the microbial community on corpses has become a research focus in the field of forensic medicine. This paper reviews the technical methods, research applications and influencing factors of microbial community in PMI estimation explored by using high-throughput sequencing technology, to provide a reference for the related research on the use of microbial community to estimate PMI.
死亡时间推断是法医学实践中的重点和难点，国内外法医学者一直在探寻客观、可量化和准确的死亡时间推断方法。随着高通量测序技术和人工智能技术的发展与结合，根据死后尸体相关的微生物群落演替规律建立死亡时间推断模型，成为法医学领域的研究热点。本文就利用高通量测序技术探究微生物群落在死亡时间推断中的技术方法、研究应用及影响因素等方面进行综述，为利用微生物群落推断死亡时间的相关研究提供参考。.
Keywords: forensic pathology; high-throughput sequencing; machine learning; microbial community; microbiomics; postmortem interval estimation; review.