Tanatomicrobioma e inteligencia artificial: la microbiología forense de hoy.

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Bibliographic Details
Title:	Tanatomicrobioma e inteligencia artificial: la microbiología forense de hoy.
Alternate Title:	Thanatomicrobiome and artificial intelligence: forensic microbiology today.
Authors:	Baltazar Ramos, Javier Iván¹ jbaltazar@uv.mx, Lizbeth, Cosme-García¹, Edmundo, Denis-Rodríguez¹
Source:	Revista Horizonte Médico. jul-sep2025, Vol. 25 Issue 3, p1-9. 9p.
Abstract (English):	Forensic microbiology enables, among other applications, the estimation of the post-mortem interval (PMI), the identification of individuals, and the location of crime scenes through microbiome analysis and the geolocation of biological remains. Artificial intelligence (AI), together with new sequencing techniques, has revolutionized this field, markedly improving the accuracy and speed of forensic analyses. In this study, a systematic review was conducted following PRISMA guidelines. Databases such as PubMed, Scopus, Web of Science, and Google Scholar were searched using keywords related to forensic microbiology, IA, and PMI. Inclusion criteria included studies published in English or Spanish, regardless of the publication date. Exclusion criteria included duplicate studies or those that did not address the thanatomicrobiome analysis using AI tools. After the search and selection process, 20 articles published between 2016 and 2024 were analyzed. The findings show that some machine learning models, such as Random Forest (RF) and Convolutional Neural Networks (CNN), provide relatively accurate estimates of the PMI. Recent studies focusing on the thanatomicrobiome are emerging as a promising tool in the forensic field, as this microbiome is unique and individualizing. These characteristics render it useful in the various stages of human identification and geolocation in criminal investigations. However, the review underscores the need for studies with larger sample sizes and for exploring the role of microorganisms beyond bacteria, in order to broaden and enhance the research landscape in this emerging field. [ABSTRACT FROM AUTHOR]
Abstract (Spanish):	La microbiología forense permite, entre otras aplicaciones, la estimación del intervalo post mortem (PMI), la identificación de individuos y la localización de escenas del crimen mediante el análisis de microbiomas y la geolocalización de restos biológicos. La inteligencia artificial (IA), junto con las nuevas técnicas de secuenciación, ha revolucionado este campo, mejorando significativamente la precisión y la rapidez de los análisis forenses. En la presente investigación se llevó a cabo una revisión sistemática, siguiendo las directrices PRISMA. Se consultaron bases de datos como PubMed, Scopus, Web of Science y Google Scholar, utilizando palabras clave relacionadas con microbiología forense, IA y PMI. Se aplicaron criterios de inclusión, como la publicación de los estudios en inglés o español y sin restricción temporal, y de exclusión, como duplicidad de publicaciones o estudios que no abordaban el análisis del tanatomicrobioma mediante herramientas de IA. Tras el proceso de búsqueda y selección, se analizaron 20 artículos publicados entre 2016 y 2024. Los hallazgos revelan que algunos modelos de aprendizaje automático, como Random Forest (RF) y las Redes Neuronales Convolucionales (CNN), permiten estimaciones relativamente precisas del PMI. Los estudios recientes enfocados en el tanatomicrobioma se perfilan como una herramienta prometedora en el ámbito forense, debido a que este microbioma es único e individualizante, lo que lo convierte en un recurso útil en las distintas etapas de la identificación humana y en los procesos de geolocalización dentro de investigaciones criminales. Sin embargo, se resalta la necesidad de realizar estudios con un mayor número de muestras y de explorar la participación de otros microorganismos, además de las bacterias, con el fin de ampliar y enriquecer el panorama de investigación en esta área emergente. [ABSTRACT FROM AUTHOR]
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Database:	MedicLatina

Description
Abstract:	Forensic microbiology enables, among other applications, the estimation of the post-mortem interval (PMI), the identification of individuals, and the location of crime scenes through microbiome analysis and the geolocation of biological remains. Artificial intelligence (AI), together with new sequencing techniques, has revolutionized this field, markedly improving the accuracy and speed of forensic analyses. In this study, a systematic review was conducted following PRISMA guidelines. Databases such as PubMed, Scopus, Web of Science, and Google Scholar were searched using keywords related to forensic microbiology, IA, and PMI. Inclusion criteria included studies published in English or Spanish, regardless of the publication date. Exclusion criteria included duplicate studies or those that did not address the thanatomicrobiome analysis using AI tools. After the search and selection process, 20 articles published between 2016 and 2024 were analyzed. The findings show that some machine learning models, such as Random Forest (RF) and Convolutional Neural Networks (CNN), provide relatively accurate estimates of the PMI. Recent studies focusing on the thanatomicrobiome are emerging as a promising tool in the forensic field, as this microbiome is unique and individualizing. These characteristics render it useful in the various stages of human identification and geolocation in criminal investigations. However, the review underscores the need for studies with larger sample sizes and for exploring the role of microorganisms beyond bacteria, in order to broaden and enhance the research landscape in this emerging field. [ABSTRACT FROM AUTHOR]
ISSN:	1727558X
DOI:	10.24265/horizmed.2025.v25n3.15